{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "439245e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-03 22:37:04.962349: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2023-04-03 22:37:04.963895: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:dynamic_embedding.GraphKeys has already been deprecated. The Variable will not be added to collections because it does not actully own any value, but only a holder of tables, which may lead to import_meta_graph failed since non-valued object has been added to collection. If you need to use `tf.compat.v1.train.Saver` and access all Variables from collection, you could manually add it to the collection by tf.compat.v1.add_to_collections(names, var) instead.\n",
      "2.8.3\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "# tfra does some patching on tensorflow so MUST be imported after importing tf\n",
    "import tensorflow_recommenders as tfrs\n",
    "import tensorflow_recommenders_addons as tfra\n",
    "import tensorflow_recommenders_addons.dynamic_embedding as de\n",
    "import tensorflow_datasets as tfds\n",
    "\n",
    "import functools\n",
    "from typing import Dict\n",
    "import dataclasses\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54499157",
   "metadata": {},
   "source": [
    "## Download datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8cc5960d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:`TensorInfo.dtype` is deprecated. Please change your code to use NumPy with the field `TensorInfo.np_dtype` or use TensorFlow with the field `TensorInfo.tf_dtype`.\n",
      "WARNING:absl:You use TensorFlow DType <dtype: 'int64'> in tfds.features This will soon be deprecated in favor of NumPy DTypes. In the meantime it was converted to int64.\n",
      "2023-04-03 22:37:50.014458: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:922] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-03 22:37:50.015554: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2023-04-03 22:37:50.016298: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublas.so.11'; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory\n",
      "2023-04-03 22:37:50.016479: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublasLt.so.11'; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory\n",
      "2023-04-03 22:37:50.017071: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcufft.so.10'; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory\n",
      "2023-04-03 22:37:50.017187: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcurand.so.10'; dlerror: libcurand.so.10: cannot open shared object file: No such file or directory\n",
      "2023-04-03 22:37:50.017385: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusolver.so.11'; dlerror: libcusolver.so.11: cannot open shared object file: No such file or directory\n",
      "2023-04-03 22:37:50.017924: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory\n",
      "2023-04-03 22:37:50.018006: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory\n",
      "2023-04-03 22:37:50.018021: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n",
      "2023-04-03 22:37:50.020158: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "WARNING:absl:`TensorInfo.dtype` is deprecated. Please change your code to use NumPy with the field `TensorInfo.np_dtype` or use TensorFlow with the field `TensorInfo.tf_dtype`.\n"
     ]
    }
   ],
   "source": [
    "# https://www.tensorflow.org/datasets/catalog/movielens\n",
    "# Interactions dataset\n",
    "raw_ratings_dataset = tfds.load(\"movielens/1m-ratings\", split=\"train\")\n",
    "# Candidates dataset\n",
    "raw_movies_dataset = tfds.load(\"movielens/1m-movies\", split=\"train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6995d5f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bucketized_user_age</th>\n",
       "      <th>movie_genres</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>user_gender</th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_occupation_label</th>\n",
       "      <th>user_occupation_text</th>\n",
       "      <th>user_rating</th>\n",
       "      <th>user_zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.0</td>\n",
       "      <td>[0, 7]</td>\n",
       "      <td>b'3107'</td>\n",
       "      <td>b'Backdraft (1991)'</td>\n",
       "      <td>977432193</td>\n",
       "      <td>True</td>\n",
       "      <td>b'130'</td>\n",
       "      <td>18</td>\n",
       "      <td>b'technician/engineer'</td>\n",
       "      <td>5.0</td>\n",
       "      <td>b'50021'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.0</td>\n",
       "      <td>[7]</td>\n",
       "      <td>b'2114'</td>\n",
       "      <td>b'Outsiders, The (1983)'</td>\n",
       "      <td>965932967</td>\n",
       "      <td>False</td>\n",
       "      <td>b'3829'</td>\n",
       "      <td>0</td>\n",
       "      <td>b'academic/educator'</td>\n",
       "      <td>4.0</td>\n",
       "      <td>b'22307'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>[4, 15]</td>\n",
       "      <td>b'256'</td>\n",
       "      <td>b'Junior (1994)'</td>\n",
       "      <td>1012103552</td>\n",
       "      <td>False</td>\n",
       "      <td>b'1265'</td>\n",
       "      <td>21</td>\n",
       "      <td>b'writer'</td>\n",
       "      <td>1.0</td>\n",
       "      <td>b'49321'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.0</td>\n",
       "      <td>[0, 10]</td>\n",
       "      <td>b'1389'</td>\n",
       "      <td>b'Jaws 3-D (1983)'</td>\n",
       "      <td>972004605</td>\n",
       "      <td>True</td>\n",
       "      <td>b'2896'</td>\n",
       "      <td>14</td>\n",
       "      <td>b'sales/marketing'</td>\n",
       "      <td>5.0</td>\n",
       "      <td>b'60073'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18.0</td>\n",
       "      <td>[0]</td>\n",
       "      <td>b'3635'</td>\n",
       "      <td>b'Spy Who Loved Me, The (1977)'</td>\n",
       "      <td>961180111</td>\n",
       "      <td>True</td>\n",
       "      <td>b'5264'</td>\n",
       "      <td>17</td>\n",
       "      <td>b'college/grad student'</td>\n",
       "      <td>4.0</td>\n",
       "      <td>b'15217'</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bucketized_user_age movie_genres movie_id                      movie_title  \\\n",
       "0                 35.0       [0, 7]  b'3107'              b'Backdraft (1991)'   \n",
       "1                 25.0          [7]  b'2114'         b'Outsiders, The (1983)'   \n",
       "2                 18.0      [4, 15]   b'256'                 b'Junior (1994)'   \n",
       "3                 18.0      [0, 10]  b'1389'               b'Jaws 3-D (1983)'   \n",
       "4                 18.0          [0]  b'3635'  b'Spy Who Loved Me, The (1977)'   \n",
       "\n",
       "    timestamp  user_gender  user_id  user_occupation_label  \\\n",
       "0   977432193         True   b'130'                     18   \n",
       "1   965932967        False  b'3829'                      0   \n",
       "2  1012103552        False  b'1265'                     21   \n",
       "3   972004605         True  b'2896'                     14   \n",
       "4   961180111         True  b'5264'                     17   \n",
       "\n",
       "      user_occupation_text  user_rating user_zip_code  \n",
       "0   b'technician/engineer'          5.0      b'50021'  \n",
       "1     b'academic/educator'          4.0      b'22307'  \n",
       "2                b'writer'          1.0      b'49321'  \n",
       "3       b'sales/marketing'          5.0      b'60073'  \n",
       "4  b'college/grad student'          4.0      b'15217'  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = tfds.as_dataframe(raw_ratings_dataset.take(100))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "92bcfbd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bucketized_user_age': <tf.Tensor: shape=(), dtype=float32, numpy=35.0>, 'movie_genres': <tf.Tensor: shape=(2,), dtype=int64, numpy=array([0, 7])>, 'movie_id': <tf.Tensor: shape=(), dtype=string, numpy=b'3107'>, 'movie_title': <tf.Tensor: shape=(), dtype=string, numpy=b'Backdraft (1991)'>, 'timestamp': <tf.Tensor: shape=(), dtype=int64, numpy=977432193>, 'user_gender': <tf.Tensor: shape=(), dtype=bool, numpy=True>, 'user_id': <tf.Tensor: shape=(), dtype=string, numpy=b'130'>, 'user_occupation_label': <tf.Tensor: shape=(), dtype=int64, numpy=18>, 'user_occupation_text': <tf.Tensor: shape=(), dtype=string, numpy=b'technician/engineer'>, 'user_rating': <tf.Tensor: shape=(), dtype=float32, numpy=5.0>, 'user_zip_code': <tf.Tensor: shape=(), dtype=string, numpy=b'50021'>}\n"
     ]
    }
   ],
   "source": [
    "for item in raw_ratings_dataset.take(1):\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c48bf97a",
   "metadata": {},
   "source": [
    "## Processing datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ce2f318c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'user_id': <tf.Tensor: shape=(), dtype=string, numpy=b'130'>, 'movie_title': <tf.Tensor: shape=(2,), dtype=string, numpy=array([b'backdraft', b'1991'], dtype=object)>}\n",
      "{'user_id': <tf.Tensor: shape=(), dtype=string, numpy=b'3829'>, 'movie_title': <tf.Tensor: shape=(3,), dtype=string, numpy=array([b'outsiders', b'the', b'1983'], dtype=object)>}\n",
      "{'user_id': <tf.Tensor: shape=(), dtype=string, numpy=b'1265'>, 'movie_title': <tf.Tensor: shape=(2,), dtype=string, numpy=array([b'junior', b'1994'], dtype=object)>}\n"
     ]
    }
   ],
   "source": [
    "max_token_length = 6\n",
    "pad_token = \"[PAD]\"\n",
    "punctuation_regex = \"[\\!\\\"#\\$%&\\(\\)\\*\\+,-\\.\\/\\:;\\<\\=\\>\\?@\\[\\]\\\\\\^_`\\{\\|\\}~\\\\t\\\\n]\"\n",
    "\n",
    "def process_text(x: tf.Tensor, max_token_length: int, punctuation_regex: str) -> tf.Tensor:\n",
    "    \n",
    "    return tf.strings.split(\n",
    "        tf.strings.regex_replace(\n",
    "            tf.strings.lower(x[\"movie_title\"]), punctuation_regex, \"\"\n",
    "        )\n",
    "    )[:max_token_length]\n",
    "\n",
    "\n",
    "def process_ratings_dataset(ratings_dataset: tf.data.Dataset) -> tf.data.Dataset:\n",
    "    \n",
    "    partial_process_text = functools.partial(\n",
    "        process_text, max_token_length=max_token_length, punctuation_regex=punctuation_regex\n",
    "    )\n",
    "\n",
    "    preprocessed_movie_title_dataset = ratings_dataset.map(\n",
    "        lambda x: partial_process_text(x)\n",
    "    )\n",
    "\n",
    "    processed_dataset = tf.data.Dataset.zip(\n",
    "        (ratings_dataset, preprocessed_movie_title_dataset)\n",
    "    ).map(\n",
    "        lambda x,y: {\"user_id\": x[\"user_id\"]} | {\"movie_title\": y}\n",
    "    )\n",
    "    \n",
    "    return processed_dataset\n",
    "\n",
    "\n",
    "def process_movies_dataset(movies_dataset: tf.data.Dataset) -> tf.data.Dataset:\n",
    "    \n",
    "    partial_process_text = functools.partial(\n",
    "        process_text, max_token_length=max_token_length, punctuation_regex=punctuation_regex\n",
    "    )\n",
    "    \n",
    "    processed_dataset = raw_movies_dataset.map(\n",
    "        lambda x: partial_process_text(x)\n",
    "    )\n",
    "    \n",
    "    return processed_dataset\n",
    "\n",
    "processed_ratings_dataset = process_ratings_dataset(raw_ratings_dataset)\n",
    "for item in processed_ratings_dataset.take(3):\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6d5deff6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:`TensorInfo.dtype` is deprecated. Please change your code to use NumPy with the field `TensorInfo.np_dtype` or use TensorFlow with the field `TensorInfo.tf_dtype`.\n",
      "WARNING:absl:`TensorInfo.dtype` is deprecated. Please change your code to use NumPy with the field `TensorInfo.np_dtype` or use TensorFlow with the field `TensorInfo.tf_dtype`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train size: 900188\n",
      "Validation size: 100021\n",
      "Train dataset size (after batching): 220\n",
      "Validation dataset size (after batching): 25\n"
     ]
    }
   ],
   "source": [
    "batch_size=4096\n",
    "seed=2023\n",
    "train_size = int(len(processed_ratings_dataset) * 0.9)\n",
    "validation_size = len(processed_ratings_dataset) - train_size\n",
    "print(f\"Train size: {train_size}\")\n",
    "print(f\"Validation size: {validation_size}\")\n",
    "\n",
    "# Things we're not considering\n",
    "# out of sample validation\n",
    "# out of time validation\n",
    "\n",
    "@dataclasses.dataclass(frozen=True)\n",
    "class TrainingDatasets:\n",
    "    train_ds: tf.data.Dataset\n",
    "    validation_ds: tf.data.Dataset\n",
    "\n",
    "@dataclasses.dataclass(frozen=True)\n",
    "class RetrievalDatasets:\n",
    "    training_datasets: TrainingDatasets\n",
    "    candidate_dataset: tf.data.Dataset\n",
    "\n",
    "def pad_and_batch_ratings_dataset(dataset: tf.data.Dataset) -> tf.data.Dataset:\n",
    "    \n",
    "    return dataset.padded_batch(\n",
    "        batch_size, \n",
    "        padded_shapes={\n",
    "            \"user_id\": tf.TensorShape([]),\n",
    "            \"movie_title\": tf.TensorShape([max_token_length,])\n",
    "        }, padding_values={\n",
    "            \"user_id\": pad_token, \n",
    "            \"movie_title\": pad_token\n",
    "        }\n",
    "    )\n",
    "    \n",
    "        \n",
    "def split_train_validation_datasets(ratings_dataset: tf.data.Dataset) -> TrainingDatasets:\n",
    "    \n",
    "    shuffled_dataset = ratings_dataset.shuffle(buffer_size=5*batch_size, seed=seed)\n",
    "    train_ds = shuffled_dataset.skip(validation_size).shuffle(buffer_size=10*batch_size).apply(pad_and_batch_ratings_dataset)\n",
    "    validation_ds = shuffled_dataset.take(validation_size).apply(pad_and_batch_ratings_dataset)\n",
    "    \n",
    "    return TrainingDatasets(train_ds=train_ds, validation_ds=validation_ds)\n",
    "\n",
    "def pad_and_batch_candidate_dataset(movies_dataset: tf.data.Dataset) -> tf.data.Dataset:\n",
    "    return movies_dataset.padded_batch(\n",
    "        batch_size, \n",
    "        padded_shapes=tf.TensorShape([max_token_length,]), \n",
    "        padding_values=pad_token\n",
    "    )\n",
    "\n",
    "def create_datasets() -> RetrievalDatasets:\n",
    "    \n",
    "    raw_ratings_dataset = tfds.load(\"movielens/1m-ratings\", split=\"train\")\n",
    "    raw_movies_dataset = tfds.load(\"movielens/1m-movies\", split=\"train\")\n",
    "    \n",
    "    processed_ratings_dataset = process_ratings_dataset(raw_ratings_dataset)\n",
    "    processed_movies_dataset = process_movies_dataset(raw_movies_dataset)\n",
    "\n",
    "    training_datasets = split_train_validation_datasets(processed_ratings_dataset)\n",
    "    candidate_dataset = pad_and_batch_candidate_dataset(processed_movies_dataset)\n",
    "    \n",
    "    return RetrievalDatasets(training_datasets=training_datasets, candidate_dataset=candidate_dataset)\n",
    "\n",
    "datasets = create_datasets()\n",
    "print(f\"Train dataset size (after batching): {len(datasets.training_datasets.train_ds)}\")\n",
    "print(f\"Validation dataset size (after batching): {len(datasets.training_datasets.validation_ds)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbe5cf3e",
   "metadata": {},
   "source": [
    "## Defining user and item towers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4abf0a30",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_user_id_lookup_layer(dataset: tf.data.Dataset) -> tf.keras.layers.Layer:\n",
    "    user_lookup_layer = tf.keras.layers.StringLookup(mask_token=None)\n",
    "    user_lookup_layer.adapt(dataset.map(lambda x: x[\"user_id\"]))\n",
    "    return user_lookup_layer\n",
    "\n",
    "def build_user_model(user_id_lookup_layer: tf.keras.layers.StringLookup):\n",
    "    vocab_size = user_id_lookup_layer.vocabulary_size()\n",
    "    return tf.keras.Sequential([\n",
    "        # Fix from https://github.com/keras-team/keras/issues/16101\n",
    "        tf.keras.layers.InputLayer(input_shape=(), dtype=tf.string),\n",
    "        user_id_lookup_layer, \n",
    "        tf.keras.layers.Embedding(vocab_size, 64), \n",
    "        tf.keras.layers.Dense(64, activation=\"gelu\"),\n",
    "        tf.keras.layers.Dense(32),\n",
    "        tf.keras.layers.Lambda(lambda x: tf.math.l2_normalize(x, axis=1))\n",
    "        \n",
    "    ], name='user_model')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "84a7c78d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_movie_title_lookup_layer(dataset: tf.data.Dataset) -> tf.keras.layers.Layer:\n",
    "    movie_title_lookup_layer = tf.keras.layers.StringLookup(mask_token=pad_token)\n",
    "    movie_title_lookup_layer.adapt(dataset.map(lambda x: x[\"movie_title\"]))\n",
    "    return movie_title_lookup_layer\n",
    "\n",
    "def build_item_model(movie_title_lookup_layer: tf.keras.layers.StringLookup):\n",
    "    vocab_size = movie_title_lookup_layer.vocabulary_size()\n",
    "    return tf.keras.models.Sequential([\n",
    "        tf.keras.layers.InputLayer(input_shape=(max_token_length), dtype=tf.string),\n",
    "        movie_title_lookup_layer, \n",
    "        tf.keras.layers.Embedding(vocab_size, 64), \n",
    "        tf.keras.layers.GlobalAveragePooling1D(),\n",
    "        tf.keras.layers.Dense(64, activation=\"gelu\"),\n",
    "        tf.keras.layers.Dense(32),\n",
    "        tf.keras.layers.Lambda(lambda x: tf.math.l2_normalize(x, axis=1))\n",
    "    ])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f941fb70",
   "metadata": {},
   "source": [
    "## Testing the lookup layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "760c762a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary size (user_id): 6041\n",
      "Vocabulary size (movie_title): 4294\n"
     ]
    }
   ],
   "source": [
    "# TODO: example models\n",
    "user_id_lookup_layer = get_user_id_lookup_layer(datasets.training_datasets.train_ds)\n",
    "print(f\"Vocabulary size (user_id): {len(user_id_lookup_layer.get_vocabulary())}\")\n",
    "\n",
    "movie_title_lookup_layer = get_movie_title_lookup_layer(datasets.training_datasets.train_ds)\n",
    "print(f\"Vocabulary size (movie_title): {len(movie_title_lookup_layer.get_vocabulary())}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca6399ce",
   "metadata": {},
   "source": [
    "## Defining the two tower model (without dynamic embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7f55b159",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TwoTowerModel(tfrs.Model):\n",
    "  # We derive from a custom base class to help reduce boilerplate. Under the hood,\n",
    "  # these are still plain Keras Models.\n",
    "\n",
    "    def __init__(self,user_model: tf.keras.Model,item_model: tf.keras.Model,task: tfrs.tasks.Retrieval):\n",
    "        super().__init__()\n",
    "\n",
    "        # Set up user and movie representations.\n",
    "        self.user_model = user_model\n",
    "        self.item_model = item_model\n",
    "        # Set up a retrieval task.\n",
    "        self.task = task\n",
    "\n",
    "    def compute_loss(self, features: Dict[str, tf.Tensor], training=False) -> tf.Tensor:\n",
    "        # Define how the loss is computed.\n",
    "        user_embeddings = self.user_model(features[\"user_id\"])\n",
    "        movie_embeddings = self.item_model(features[\"movie_title\"])\n",
    "        return self.task(user_embeddings, movie_embeddings)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddab7e52",
   "metadata": {},
   "source": [
    "## Creating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b451e66e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:`TensorInfo.dtype` is deprecated. Please change your code to use NumPy with the field `TensorInfo.np_dtype` or use TensorFlow with the field `TensorInfo.tf_dtype`.\n",
      "WARNING:absl:`TensorInfo.dtype` is deprecated. Please change your code to use NumPy with the field `TensorInfo.np_dtype` or use TensorFlow with the field `TensorInfo.tf_dtype`.\n"
     ]
    }
   ],
   "source": [
    "def create_two_tower_model(dataset: tf.data.Dataset, candidate_dataset: tf.data.Dataset) -> tf.keras.Model:\n",
    "    user_id_lookup_layer = get_user_id_lookup_layer(dataset)\n",
    "    movie_title_lookup_layer = get_movie_title_lookup_layer(dataset)\n",
    "    user_model = build_user_model(user_id_lookup_layer)\n",
    "    item_model = build_item_model(movie_title_lookup_layer)\n",
    "    task = tfrs.tasks.Retrieval(\n",
    "        metrics=tfrs.metrics.FactorizedTopK(\n",
    "            candidate_dataset.map(item_model)\n",
    "        ),\n",
    "    )\n",
    "\n",
    "    model = TwoTowerModel(user_model, item_model, task)\n",
    "    model.compile(optimizer=tf.keras.optimizers.Adam())\n",
    "    \n",
    "    return model\n",
    "\n",
    "datasets = create_datasets()\n",
    "model = create_two_tower_model(datasets.training_datasets.train_ds, datasets.candidate_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13fbae80",
   "metadata": {},
   "source": [
    "## Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3108ca07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "220/220 [==============================] - 159s 689ms/step - factorized_top_k/top_1_categorical_accuracy: 4.0880e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0034 - factorized_top_k/top_10_categorical_accuracy: 0.0071 - factorized_top_k/top_50_categorical_accuracy: 0.0325 - factorized_top_k/top_100_categorical_accuracy: 0.0597 - loss: 33607.3019 - regularization_loss: 0.0000e+00 - total_loss: 33607.3019 - val_factorized_top_k/top_1_categorical_accuracy: 0.0010 - val_factorized_top_k/top_5_categorical_accuracy: 0.0070 - val_factorized_top_k/top_10_categorical_accuracy: 0.0137 - val_factorized_top_k/top_50_categorical_accuracy: 0.0595 - val_factorized_top_k/top_100_categorical_accuracy: 0.1069 - val_loss: 12435.4785 - val_regularization_loss: 0.0000e+00 - val_total_loss: 12435.4785\n",
      "Epoch 2/3\n",
      "220/220 [==============================] - 166s 720ms/step - factorized_top_k/top_1_categorical_accuracy: 9.3092e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0075 - factorized_top_k/top_10_categorical_accuracy: 0.0153 - factorized_top_k/top_50_categorical_accuracy: 0.0669 - factorized_top_k/top_100_categorical_accuracy: 0.1179 - loss: 33047.6685 - regularization_loss: 0.0000e+00 - total_loss: 33047.6685 - val_factorized_top_k/top_1_categorical_accuracy: 0.0012 - val_factorized_top_k/top_5_categorical_accuracy: 0.0074 - val_factorized_top_k/top_10_categorical_accuracy: 0.0157 - val_factorized_top_k/top_50_categorical_accuracy: 0.0667 - val_factorized_top_k/top_100_categorical_accuracy: 0.1174 - val_loss: 12380.8525 - val_regularization_loss: 0.0000e+00 - val_total_loss: 12380.8525\n",
      "Epoch 3/3\n",
      "220/220 [==============================] - 164s 717ms/step - factorized_top_k/top_1_categorical_accuracy: 9.8424e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0082 - factorized_top_k/top_10_categorical_accuracy: 0.0166 - factorized_top_k/top_50_categorical_accuracy: 0.0720 - factorized_top_k/top_100_categorical_accuracy: 0.1260 - loss: 32993.3375 - regularization_loss: 0.0000e+00 - total_loss: 32993.3375 - val_factorized_top_k/top_1_categorical_accuracy: 0.0012 - val_factorized_top_k/top_5_categorical_accuracy: 0.0078 - val_factorized_top_k/top_10_categorical_accuracy: 0.0164 - val_factorized_top_k/top_50_categorical_accuracy: 0.0714 - val_factorized_top_k/top_100_categorical_accuracy: 0.1245 - val_loss: 12369.2646 - val_regularization_loss: 0.0000e+00 - val_total_loss: 12369.2646\n"
     ]
    }
   ],
   "source": [
    "# Train for 3 epochs.\n",
    "\n",
    "history = model.fit(datasets.training_datasets.train_ds, epochs=3, validation_data=datasets.training_datasets.validation_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7308cfd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "history_standard = history.history"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c76504f",
   "metadata": {},
   "source": [
    "## Using `Embedding` layer from `dynamic_embedding`\n",
    "\n",
    "Defining user tower and iterm tower with dynamic embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e9503e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.compat.v1.reset_default_graph()\n",
    "\n",
    "def build_de_user_model(user_id_lookup_layer: tf.keras.layers.StringLookup) -> tf.keras.layers.Layer:\n",
    "    vocab_size = user_id_lookup_layer.vocabulary_size()\n",
    "    return tf.keras.Sequential([\n",
    "        # Fix from https://github.com/keras-team/keras/issues/16101\n",
    "        tf.keras.layers.InputLayer(input_shape=(), dtype=tf.string),\n",
    "        user_id_lookup_layer, \n",
    "        de.keras.layers.Embedding(\n",
    "            embedding_size=64,\n",
    "            initializer=tf.random_uniform_initializer(),\n",
    "            init_capacity=int(vocab_size*0.8), \n",
    "            restrict_policy=de.FrequencyRestrictPolicy,\n",
    "            name=\"UserDynamicEmbeddingLayer\"\n",
    "        ), \n",
    "        tf.keras.layers.Dense(64, activation=\"gelu\"),\n",
    "        tf.keras.layers.Dense(32),\n",
    "        tf.keras.layers.Lambda(lambda x: tf.math.l2_normalize(x, axis=1))\n",
    "        \n",
    "    ], name='user_model')\n",
    "\n",
    "def build_de_item_model(movie_title_lookup_layer: tf.keras.layers.StringLookup) -> tf.keras.layers.Layer:\n",
    "    vocab_size = movie_title_lookup_layer.vocabulary_size()\n",
    "    return tf.keras.models.Sequential([\n",
    "        tf.keras.layers.InputLayer(input_shape=(max_token_length), dtype=tf.string),\n",
    "        movie_title_lookup_layer, \n",
    "        de.keras.layers.SquashedEmbedding(\n",
    "            embedding_size=64,\n",
    "            initializer=tf.random_uniform_initializer(),\n",
    "            init_capacity=int(vocab_size*0.8), \n",
    "            restrict_policy=de.FrequencyRestrictPolicy,\n",
    "            combiner=\"mean\",\n",
    "            name=\"ItemDynamicEmbeddingLayer\"\n",
    "        ),\n",
    "        tf.keras.layers.Dense(64, activation=\"gelu\"),\n",
    "        tf.keras.layers.Dense(32),\n",
    "        tf.keras.layers.Lambda(lambda x: tf.math.l2_normalize(x, axis=1))\n",
    "    ])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5070bc38",
   "metadata": {},
   "source": [
    "## Defining the callback to control and log dynamic embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dcae0f22",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DynamicEmbeddingCallback(tf.keras.callbacks.Callback):\n",
    "    \n",
    "    def __init__(self, model, steps_per_logging, steps_per_restrict=None, restrict=False):\n",
    "        self.model = model\n",
    "        self.steps_per_logging = steps_per_logging\n",
    "        self.steps_per_restrict = steps_per_restrict\n",
    "        self.restrict = restrict\n",
    "    \n",
    "    def on_train_begin(self, logs=None):\n",
    "        self.model.dynamic_embedding_history = {}\n",
    "        \n",
    "    def on_train_batch_end(self, batch, logs=None):\n",
    "                \n",
    "        if self.restrict and self.steps_per_restrict and (batch+1) % self.steps_per_restrict == 0:\n",
    "            \n",
    "            [\n",
    "                self.model.embedding_layers[k].params.restrict(\n",
    "                    int(self.model.lookup_vocab_sizes[k]*0.8), \n",
    "                    trigger=self.model.lookup_vocab_sizes[k]-2 # UNK & PAD tokens\n",
    "                ) for k in self.model.embedding_layers.keys()\n",
    "            ] \n",
    "        \n",
    "        if (batch+1) % self.steps_per_logging == 0:\n",
    "            \n",
    "            embedding_size_dict = {\n",
    "                k:self.model.embedding_layers[k].params.size().numpy() \n",
    "                for k in self.model.embedding_layers.keys()\n",
    "            }\n",
    "\n",
    "            for k, v in embedding_size_dict.items():\n",
    "                self.model.dynamic_embedding_history.setdefault(f\"embedding_size_{k}\", []).append(v)\n",
    "            self.model.dynamic_embedding_history.setdefault(f\"step\", []).append(batch+1)\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b85a20c2",
   "metadata": {},
   "source": [
    "## Defining two tower model with dynamic embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3f62219a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DynamicEmbeddingTwoTowerModel(tfrs.Model):\n",
    "  # We derive from a custom base class to help reduce boilerplate. Under the hood,\n",
    "  # these are still plain Keras Models.\n",
    "\n",
    "    def __init__(self,user_model: tf.keras.Model,item_model: tf.keras.Model,task: tfrs.tasks.Retrieval):\n",
    "        super().__init__()\n",
    "\n",
    "        # Set up user and movie representations.\n",
    "        self.user_model = user_model\n",
    "        self.item_model = item_model\n",
    "        \n",
    "        self.embedding_layers = {\n",
    "            \"user\": user_model.layers[1], \n",
    "            \"movie\": item_model.layers[1]\n",
    "        }\n",
    "        \n",
    "        if not all([\"embedding\" in v.name.lower() for k,v in self.embedding_layers.items()]):\n",
    "            raise TypeError(\n",
    "                f\"\"\"All layers in embedding_layers must be embedding layers.\n",
    "                Got {[v.name for v in self.embedding_layers.values()]}\"\"\"\n",
    "            )\n",
    "            \n",
    "        self.lookup_vocab_sizes = {\n",
    "            \"user\": user_model.layers[0].vocabulary_size(), \n",
    "            \"movie\": item_model.layers[0].vocabulary_size()\n",
    "        }\n",
    "        self.dynamic_embedding_history = {}\n",
    "        # Set up a retrieval task.\n",
    "        self.task = task\n",
    "\n",
    "    def compute_loss(self, features: Dict[str, tf.Tensor], training=False) -> tf.Tensor:\n",
    "        # Define how the loss is computed.\n",
    "        user_embeddings = self.user_model(features[\"user_id\"])\n",
    "        movie_embeddings = self.item_model(features[\"movie_title\"])\n",
    "        return self.task(user_embeddings, movie_embeddings)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "91a3d7c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:`TensorInfo.dtype` is deprecated. Please change your code to use NumPy with the field `TensorInfo.np_dtype` or use TensorFlow with the field `TensorInfo.tf_dtype`.\n",
      "WARNING:absl:`TensorInfo.dtype` is deprecated. Please change your code to use NumPy with the field `TensorInfo.np_dtype` or use TensorFlow with the field `TensorInfo.tf_dtype`.\n",
      "2023-04-03 22:38:26.783370: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:390] Filling up shuffle buffer (this may take a while): 34749 of 40960\n",
      "2023-04-03 22:38:27.186988: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:415] Shuffle buffer filled.\n",
      "2023-04-03 22:39:30.453738: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:390] Filling up shuffle buffer (this may take a while): 39970 of 40960\n",
      "2023-04-03 22:39:30.515715: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:415] Shuffle buffer filled.\n",
      "2023-04-03 22:40:19.388437: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:922] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-03 22:40:19.388571: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n",
      "2023-04-03 22:40:21.278577: I ./tensorflow_recommenders_addons/dynamic_embedding/core/kernels/lookup_impl/lookup_table_op_cpu.h:157] HashTable on CPU is created on optimized mode: K=l, V=i, DIM=1, init_size=4832\n",
      "2023-04-03 22:40:21.289981: I ./tensorflow_recommenders_addons/dynamic_embedding/core/kernels/lookup_impl/lookup_table_op_cpu.h:157] HashTable on CPU is created on optimized mode: K=l, V=f, DIM=64, init_size=4832\n",
      "2023-04-03 22:40:21.556787: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:922] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-03 22:40:21.556877: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n",
      "2023-04-03 22:40:21.557830: I ./tensorflow_recommenders_addons/dynamic_embedding/core/kernels/lookup_impl/lookup_table_op_cpu.h:157] HashTable on CPU is created on optimized mode: K=l, V=i, DIM=1, init_size=3435\n",
      "2023-04-03 22:40:21.560738: I ./tensorflow_recommenders_addons/dynamic_embedding/core/kernels/lookup_impl/lookup_table_op_cpu.h:157] HashTable on CPU is created on optimized mode: K=l, V=f, DIM=64, init_size=3435\n"
     ]
    }
   ],
   "source": [
    "def create_de_two_tower_model(dataset: tf.data.Dataset, candidate_dataset: tf.data.Dataset) -> tf.keras.Model:\n",
    "    \n",
    "    user_id_lookup_layer = get_user_id_lookup_layer(dataset)\n",
    "    movie_title_lookup_layer = get_movie_title_lookup_layer(dataset)\n",
    "    user_model = build_de_user_model(user_id_lookup_layer)\n",
    "    item_model = build_de_item_model(movie_title_lookup_layer)\n",
    "    task = tfrs.tasks.Retrieval(\n",
    "        metrics=tfrs.metrics.FactorizedTopK(\n",
    "            candidate_dataset.map(item_model)\n",
    "        ),\n",
    "    )\n",
    "\n",
    "    model = DynamicEmbeddingTwoTowerModel(user_model, item_model, task)\n",
    "    optimizer = de.DynamicEmbeddingOptimizer(tf.keras.optimizers.Adam())\n",
    "    model.compile(optimizer=optimizer)\n",
    "    \n",
    "    return model\n",
    "\n",
    "datasets = create_datasets()\n",
    "de_model = create_de_two_tower_model(datasets.training_datasets.train_ds, datasets.candidate_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6471143",
   "metadata": {},
   "source": [
    "## Training the model with dynamic embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5569c353",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:`TensorInfo.dtype` is deprecated. Please change your code to use NumPy with the field `TensorInfo.np_dtype` or use TensorFlow with the field `TensorInfo.tf_dtype`.\n",
      "WARNING:absl:`TensorInfo.dtype` is deprecated. Please change your code to use NumPy with the field `TensorInfo.np_dtype` or use TensorFlow with the field `TensorInfo.tf_dtype`.\n",
      "2023-03-27 06:15:45.229098: I ./tensorflow_recommenders_addons/dynamic_embedding/core/kernels/lookup_impl/lookup_table_op_cpu.h:157] HashTable on CPU is created on optimized mode: K=l, V=f, DIM=64, init_size=4832\n",
      "2023-03-27 06:15:45.232571: I ./tensorflow_recommenders_addons/dynamic_embedding/core/kernels/lookup_impl/lookup_table_op_cpu.h:157] HashTable on CPU is created on optimized mode: K=l, V=f, DIM=64, init_size=3435\n",
      "2023-03-27 06:15:45.242801: I ./tensorflow_recommenders_addons/dynamic_embedding/core/kernels/lookup_impl/lookup_table_op_cpu.h:157] HashTable on CPU is created on optimized mode: K=l, V=f, DIM=64, init_size=4832\n",
      "2023-03-27 06:15:45.250305: I ./tensorflow_recommenders_addons/dynamic_embedding/core/kernels/lookup_impl/lookup_table_op_cpu.h:157] HashTable on CPU is created on optimized mode: K=l, V=f, DIM=64, init_size=3435\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "220/220 [==============================] - 165s 710ms/step - factorized_top_k/top_1_categorical_accuracy: 3.3326e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0030 - factorized_top_k/top_10_categorical_accuracy: 0.0060 - factorized_top_k/top_50_categorical_accuracy: 0.0279 - factorized_top_k/top_100_categorical_accuracy: 0.0526 - loss: 33681.4889 - regularization_loss: 0.0000e+00 - total_loss: 33681.4889 - val_factorized_top_k/top_1_categorical_accuracy: 0.0010 - val_factorized_top_k/top_5_categorical_accuracy: 0.0067 - val_factorized_top_k/top_10_categorical_accuracy: 0.0138 - val_factorized_top_k/top_50_categorical_accuracy: 0.0590 - val_factorized_top_k/top_100_categorical_accuracy: 0.1057 - val_loss: 12417.9785 - val_regularization_loss: 0.0000e+00 - val_total_loss: 12417.9785\n",
      "220/220 [==============================] - 166s 723ms/step - factorized_top_k/top_1_categorical_accuracy: 8.7648e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0075 - factorized_top_k/top_10_categorical_accuracy: 0.0152 - factorized_top_k/top_50_categorical_accuracy: 0.0661 - factorized_top_k/top_100_categorical_accuracy: 0.1170 - loss: 33052.0830 - regularization_loss: 0.0000e+00 - total_loss: 33052.0830 - val_factorized_top_k/top_1_categorical_accuracy: 0.0010 - val_factorized_top_k/top_5_categorical_accuracy: 0.0074 - val_factorized_top_k/top_10_categorical_accuracy: 0.0148 - val_factorized_top_k/top_50_categorical_accuracy: 0.0671 - val_factorized_top_k/top_100_categorical_accuracy: 0.1187 - val_loss: 12345.8174 - val_regularization_loss: 0.0000e+00 - val_total_loss: 12345.8174\n",
      "220/220 [==============================] - 163s 709ms/step - factorized_top_k/top_1_categorical_accuracy: 9.8202e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0079 - factorized_top_k/top_10_categorical_accuracy: 0.0161 - factorized_top_k/top_50_categorical_accuracy: 0.0721 - factorized_top_k/top_100_categorical_accuracy: 0.1269 - loss: 32986.5514 - regularization_loss: 0.0000e+00 - total_loss: 32986.5514 - val_factorized_top_k/top_1_categorical_accuracy: 0.0011 - val_factorized_top_k/top_5_categorical_accuracy: 0.0077 - val_factorized_top_k/top_10_categorical_accuracy: 0.0159 - val_factorized_top_k/top_50_categorical_accuracy: 0.0699 - val_factorized_top_k/top_100_categorical_accuracy: 0.1243 - val_loss: 12334.5811 - val_regularization_loss: 0.0000e+00 - val_total_loss: 12334.5811\n"
     ]
    }
   ],
   "source": [
    "epochs = 3\n",
    "\n",
    "history_de = {}\n",
    "history_de_size = {}\n",
    "de_callback = DynamicEmbeddingCallback(de_model, steps_per_logging=20)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "\n",
    "    datasets = create_datasets()\n",
    "    train_steps = len(datasets.training_datasets.train_ds)\n",
    "    \n",
    "    hist = de_model.fit(\n",
    "        datasets.training_datasets.train_ds, \n",
    "        epochs=1, \n",
    "        validation_data=datasets.training_datasets.validation_ds, \n",
    "        callbacks=[de_callback]\n",
    "    )\n",
    "    \n",
    "    for k,v in de_model.dynamic_embedding_history.items():\n",
    "        if k==\"step\":\n",
    "            v = [vv+(epoch*train_steps) for vv in v]\n",
    "        history_de_size.setdefault(k, []).extend(v)\n",
    "        \n",
    "    for k,v in hist.history.items():\n",
    "        history_de.setdefault(k, []).extend(v)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2650791",
   "metadata": {},
   "source": [
    "## Training the model with dynamic embeddings (streaming with restrict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e05286e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-27 06:25:24.033027: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:922] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-03-27 06:25:24.033162: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n",
      "2023-03-27 06:25:24.034944: I ./tensorflow_recommenders_addons/dynamic_embedding/core/kernels/lookup_impl/lookup_table_op_cpu.h:157] HashTable on CPU is created on optimized mode: K=l, V=i, DIM=1, init_size=4832\n",
      "2023-03-27 06:25:24.037935: I ./tensorflow_recommenders_addons/dynamic_embedding/core/kernels/lookup_impl/lookup_table_op_cpu.h:157] HashTable on CPU is created on optimized mode: K=l, V=f, DIM=64, init_size=4832\n",
      "2023-03-27 06:25:24.070877: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:922] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-03-27 06:25:24.070932: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n",
      "2023-03-27 06:25:24.071565: I ./tensorflow_recommenders_addons/dynamic_embedding/core/kernels/lookup_impl/lookup_table_op_cpu.h:157] HashTable on CPU is created on optimized mode: K=l, V=i, DIM=1, init_size=3435\n",
      "2023-03-27 06:25:24.073115: I ./tensorflow_recommenders_addons/dynamic_embedding/core/kernels/lookup_impl/lookup_table_op_cpu.h:157] HashTable on CPU is created on optimized mode: K=l, V=f, DIM=64, init_size=3435\n",
      "2023-03-27 06:25:24.442513: I ./tensorflow_recommenders_addons/dynamic_embedding/core/kernels/lookup_impl/lookup_table_op_cpu.h:157] HashTable on CPU is created on optimized mode: K=l, V=f, DIM=64, init_size=4832\n",
      "2023-03-27 06:25:24.446265: I ./tensorflow_recommenders_addons/dynamic_embedding/core/kernels/lookup_impl/lookup_table_op_cpu.h:157] HashTable on CPU is created on optimized mode: K=l, V=f, DIM=64, init_size=3435\n",
      "2023-03-27 06:25:24.449529: I ./tensorflow_recommenders_addons/dynamic_embedding/core/kernels/lookup_impl/lookup_table_op_cpu.h:157] HashTable on CPU is created on optimized mode: K=l, V=f, DIM=64, init_size=4832\n",
      "2023-03-27 06:25:24.452668: I ./tensorflow_recommenders_addons/dynamic_embedding/core/kernels/lookup_impl/lookup_table_op_cpu.h:157] HashTable on CPU is created on optimized mode: K=l, V=f, DIM=64, init_size=3435\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "220/220 [==============================] - 166s 719ms/step - factorized_top_k/top_1_categorical_accuracy: 3.6881e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0030 - factorized_top_k/top_10_categorical_accuracy: 0.0062 - factorized_top_k/top_50_categorical_accuracy: 0.0296 - factorized_top_k/top_100_categorical_accuracy: 0.0554 - loss: 33632.4752 - regularization_loss: 0.0000e+00 - total_loss: 33632.4752 - val_factorized_top_k/top_1_categorical_accuracy: 7.1985e-04 - val_factorized_top_k/top_5_categorical_accuracy: 0.0054 - val_factorized_top_k/top_10_categorical_accuracy: 0.0109 - val_factorized_top_k/top_50_categorical_accuracy: 0.0495 - val_factorized_top_k/top_100_categorical_accuracy: 0.0898 - val_loss: 12426.0557 - val_regularization_loss: 0.0000e+00 - val_total_loss: 12426.0557\n",
      "220/220 [==============================] - 177s 765ms/step - factorized_top_k/top_1_categorical_accuracy: 8.8981e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0073 - factorized_top_k/top_10_categorical_accuracy: 0.0147 - factorized_top_k/top_50_categorical_accuracy: 0.0627 - factorized_top_k/top_100_categorical_accuracy: 0.1106 - loss: 33060.9104 - regularization_loss: 0.0000e+00 - total_loss: 33060.9104 - val_factorized_top_k/top_1_categorical_accuracy: 0.0011 - val_factorized_top_k/top_5_categorical_accuracy: 0.0077 - val_factorized_top_k/top_10_categorical_accuracy: 0.0148 - val_factorized_top_k/top_50_categorical_accuracy: 0.0650 - val_factorized_top_k/top_100_categorical_accuracy: 0.1141 - val_loss: 12372.0713 - val_regularization_loss: 0.0000e+00 - val_total_loss: 12372.0713\n",
      "220/220 [==============================] - 160s 684ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0011 - factorized_top_k/top_5_categorical_accuracy: 0.0083 - factorized_top_k/top_10_categorical_accuracy: 0.0171 - factorized_top_k/top_50_categorical_accuracy: 0.0732 - factorized_top_k/top_100_categorical_accuracy: 0.1278 - loss: 32981.5708 - regularization_loss: 0.0000e+00 - total_loss: 32981.5708 - val_factorized_top_k/top_1_categorical_accuracy: 0.0010 - val_factorized_top_k/top_5_categorical_accuracy: 0.0078 - val_factorized_top_k/top_10_categorical_accuracy: 0.0162 - val_factorized_top_k/top_50_categorical_accuracy: 0.0684 - val_factorized_top_k/top_100_categorical_accuracy: 0.1205 - val_loss: 12357.4219 - val_regularization_loss: 0.0000e+00 - val_total_loss: 12357.4219\n"
     ]
    }
   ],
   "source": [
    "tf.compat.v1.reset_default_graph()\n",
    "\n",
    "datasets = create_datasets()\n",
    "de_model = create_de_two_tower_model(datasets.training_datasets.train_ds, datasets.candidate_dataset)\n",
    "\n",
    "epochs = 3\n",
    "\n",
    "history_de_restrict = {}\n",
    "history_de_size_restrict = {}\n",
    "de_callback = DynamicEmbeddingCallback(de_model, steps_per_logging=20, steps_per_restrict=220, restrict=True)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "\n",
    "    datasets = create_datasets()\n",
    "    train_steps = len(datasets.training_datasets.train_ds)\n",
    "    \n",
    "    hist = de_model.fit(\n",
    "        datasets.training_datasets.train_ds, \n",
    "        epochs=1, \n",
    "        validation_data=datasets.training_datasets.validation_ds, \n",
    "        callbacks=[de_callback]\n",
    "    )\n",
    "    \n",
    "    for k,v in de_model.dynamic_embedding_history.items():\n",
    "        if k==\"step\":\n",
    "            v = [vv+(epoch*train_steps) for vv in v]\n",
    "        history_de_size_restrict.setdefault(k, []).extend(v)\n",
    "        \n",
    "    for k,v in hist.history.items():\n",
    "        history_de_restrict.setdefault(k, []).extend(v)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c84bc203",
   "metadata": {},
   "source": [
    "## Plotting accuracies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e70eaec0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Model accuracy with and wo dynamic embeddings')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkAAAAHHCAYAAABXx+fLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADldklEQVR4nOzdd1iV5RvA8e9hoywVZAgC7r1wmwvNbblNK0eWDTXN/JW2y0rLTHM0bGjDXDkqU1PBvRX3ShQUEFBUpsg67++PRw4cGTIF9P5c17mE9zznfZ9zOHJunnHfOk3TNIQQQgghHiEmJd0BIYQQQogHTQIgIYQQQjxyJAASQgghxCNHAiAhhBBCPHIkABJCCCHEI0cCICGEEEI8ciQAEkIIIcQjRwIgIYQQQjxyJAASQgghxCNHAiCRLzqdjg8++CDfjwsODkan07FkyZIi75MovA8++ACdTpevtlFRUcXcq8Ipi++5JUuWoNPpCA4OLumuFImC/r4oSaNGjcLGxuaBXMvLy4tRo0bdt11274tOnTrRqVOnYuvbo0ACoDIo/T+DTqdj9+7dWe7XNA0PDw90Oh19+vQpgR6Kh8Gnn37KunXrSrobQghRLCQAKsOsrKz4/fffsxzfsWMHoaGhWFpalkCvRFn0zjvvkJiYaHRMAiBRWImJibzzzjsl3Y2H0ubNm9m8eXNJd6NMkwCoDOvVqxerVq0iNTXV6Pjvv/+Oj48PLi4uJdSzR0dCQkJJd6FImJmZYWVlVdLdEA8ZKysrzMzMSrobDyULCwssLCxKuhtlmgRAZdiwYcO4ceMGW7ZsMRxLTk7mjz/+YPjw4dk+JiEhgddffx0PDw8sLS2pXbs2X3zxBZqmGbVLSkritddew8nJCVtbW5544glCQ0OzPWdYWBjPPfcczs7OWFpaUr9+fX766acCPaebN28yZcoUGjZsiI2NDXZ2dvTs2ZPjx49naXvnzh0++OADatWqhZWVFa6urgwYMICLFy8a2uj1er766isaNmyIlZUVTk5O9OjRg8OHDwO5rxO5d/1C+tqXM2fOMHz4cCpUqMBjjz0GwIkTJxg1ahTVqlXDysoKFxcXnnvuOW7cuJHt6zVmzBjc3NywtLTE29ubl19+meTkZC5duoROp2POnDlZHrd37150Oh3Lli3L9rXTNA1HR0cmT55s9PwdHBwwNTUlOjracPyzzz7DzMyM+Ph4o+eW+bknJCTw888/G6Zb712rEB0dzahRo3BwcMDe3p7Ro0dz+/btbPuW2a5duxg8eDBVq1bF0tISDw8PXnvttSwjUOlrMcLCwujXrx82NjY4OTkxZcoU0tLSsu2Lvb09Dg4OjBw50uj55iQ6OhpTU1PmzZtnOBYVFYWJiQmVKlUy+n/x8ssvZ/mjYtWqVfj4+GBtbY2joyPPPPMMYWFh970uwOnTp/H19cXa2hp3d3c+/vhj9Hq9UZuRI0fi6OhISkpKlsd369aN2rVrG77X6XSMHz+edevW0aBBA8P/xU2bNhk97vLly7zyyivUrl0ba2trKlWqxODBg7OsO0qfat+9ezevvvoqTk5OODg48OKLL5KcnEx0dDQjRoygQoUKVKhQgTfeeCPL75Hs1gDl9v7PjV6vZ+7cudSvXx8rKyucnZ158cUXuXXrllE7Ly8v+vTpw/bt22nevDnW1tY0bNiQ7du3A7BmzRrD7wMfHx+OHj2a7fUuXbpE9+7dKV++PG5ubnz00UdZnl9e+6RpGh9//DHu7u6UK1eOzp07c/r06Wyvm5f3BWRdA7R9+3Z0Oh0rV67kk08+wd3dHSsrK7p06UJgYGCWxy9cuJBq1aphbW1Ny5Yt2bVrV7briubPn0/9+vUpV64cFSpUoHnz5tnOPJRFEpqXYV5eXrRp04Zly5bRs2dPADZu3EhMTAxPPfWU0S91UP8Jn3jiCbZt28aYMWNo0qQJ//77L//73/8ICwsz+tB9/vnn+e233xg+fDht27bF39+f3r17Z+lDZGQkrVu3NvzydXJyYuPGjYwZM4bY2FgmTZqUr+d06dIl1q1bx+DBg/H29iYyMpLvvvuOjh07cubMGdzc3ABIS0ujT58++Pn58dRTTzFx4kTi4uLYsmULp06donr16gCMGTOGJUuW0LNnT55//nlSU1PZtWsX+/fvp3nz5vnqW7rBgwdTs2ZNPv30U8MvxC1btnDp0iVGjx6Ni4sLp0+fZtGiRZw+fZr9+/cbgourV6/SsmVLoqOjGTt2LHXq1CEsLIw//viD27dvU61aNdq1a8fSpUt57bXXjK67dOlSbG1tefLJJ7Ptl06no127duzcudNw7MSJE8TExGBiYsKePXsMP8Ndu3bRtGnTHBd7/vrrrzz//PO0bNmSsWPHAhhe03RDhgzB29ubGTNmEBAQwA8//EDlypX57LPPcn39Vq1axe3bt3n55ZepVKkSBw8eZP78+YSGhrJq1SqjtmlpaXTv3p1WrVrxxRdfsHXrVmbPnk316tV5+eWXAfW+fvLJJ9m9ezcvvfQSdevWZe3atYwcOTLXfgA4ODjQoEEDdu7cyauvvgrA7t270el03Lx5kzNnzlC/fn3Da9a+fXvDY5csWcLo0aNp0aIFM2bMIDIykq+++oo9e/Zw9OhRHBwccrxuREQEnTt3JjU1lalTp1K+fHkWLVqEtbW1Ubtnn32WX375hX///ddoPV9ERAT+/v68//77Ru13797NmjVreOWVV7C1tWXevHkMHDiQK1euUKlSJQAOHTrE3r17eeqpp3B3dyc4OJhvvvmGTp06cebMGcqVK2d0zgkTJuDi4sKHH37I/v37WbRoEQ4ODuzdu5eqVavy6aefsmHDBmbNmkWDBg0YMWJEjs/7fu//3EY0XnzxRcNr/uqrrxIUFMSCBQs4evQoe/bswdzc3NA2MDCQ4cOH8+KLL/LMM8/wxRdf0LdvX7799lveeustXnnlFQBmzJjBkCFDOH/+PCYmGeMBaWlp9OjRg9atW/P555+zadMm3n//fVJTU/noo4/y3af33nuPjz/+mF69etGrVy8CAgLo1q1blqAvr++L3MycORMTExOmTJlCTEwMn3/+OU8//TQHDhwwtPnmm28YP3487du357XXXiM4OJh+/fpRoUIF3N3dDe2+//57Xn31VQYNGsTEiRO5c+cOJ06c4MCBAzn+kV2maKLMWbx4sQZohw4d0hYsWKDZ2tpqt2/f1jRN0wYPHqx17txZ0zRN8/T01Hr37m143Lp16zRA+/jjj43ON2jQIE2n02mBgYGapmnasWPHNEB75ZVXjNoNHz5cA7T333/fcGzMmDGaq6urFhUVZdT2qaee0uzt7Q39CgoK0gBt8eLFuT63O3fuaGlpaUbHgoKCNEtLS+2jjz4yHPvpp580QPvyyy+znEOv12uapmn+/v4aoL366qs5tsmtX/c+1/fff18DtGHDhmVpm/48M1u2bJkGaDt37jQcGzFihGZiYqIdOnQoxz599913GqCdPXvWcF9ycrLm6OiojRw5MsvjMps1a5ZmamqqxcbGapqmafPmzdM8PT21li1bam+++aamaZqWlpamOTg4aK+99lqW55ZZ+fLls71eetvnnnvO6Hj//v21SpUq5do/Tcv+tZoxY4am0+m0y5cvG46NHDlSA4x+7pqmaU2bNtV8fHwM36e/rz///HPDsdTUVK19+/Z5es+NGzdOc3Z2Nnw/efJkrUOHDlrlypW1b775RtM0Tbtx44am0+m0r776StM09fOoXLmy1qBBAy0xMdHw2PXr12uA9t577+V6zUmTJmmAduDAAcOxa9euafb29hqgBQUFaZqmflbu7u7a0KFDjR7/5ZdfajqdTrt06ZLhGKBZWFgY/h9rmqYdP35cA7T58+cbjmX3+u/bt08DtF9++cVwLP33TPfu3Q3vTU3TtDZt2mg6nU576aWXDMdSU1M1d3d3rWPHjkbnvff/UF7e/9nZtWuXBmhLly41Or5p06Ysxz09PTVA27t3r+HYv//+qwGatbW10Xss/f/atm3bDMfS33cTJkww6lvv3r01CwsL7fr16/nq07Vr1zQLCwutd+/eRs/xrbfe0gCj/2N5fV9omqZ17NjR6PXetm2bBmh169bVkpKSDMe/+uorDdBOnjypaZqmJSUlaZUqVdJatGihpaSkGNotWbJEA4zO+eSTT2r169fXHlYyBVbGDRkyhMTERNavX09cXBzr16/PMTLfsGEDpqamhr90073++utomsbGjRsN7YAs7e4dzdE0jdWrV9O3b180TSMqKspw6969OzExMQQEBOTr+VhaWhr+EktLS+PGjRvY2NhQu3Zto3OtXr0aR0dHJkyYkOUc6aMtq1evRqfTZfkrOXObgnjppZeyHMv8F9qdO3eIioqidevWAIZ+6/V61q1bR9++fbMdfUrv05AhQ7CysmLp0qWG+/7991+ioqJ45plncu1b+/btSUtLY+/evUDGqEX79u3ZtWsXAKdOnSI6OtpoNKMg7n0d2rdvz40bN4iNjc31cZlfq4SEBKKiomjbti2apmU7HZHddS5dumT4fsOGDZiZmRlGhABMTU2zfW9kp3379kRGRnL+/HlAvWYdOnQwes12796NpmmG1+zw4cNcu3aNV155xWjtVO/evalTpw7//PNPrtfcsGEDrVu3pmXLloZjTk5OPP3000btTExMePrpp/nrr7+Ii4szHF+6dClt27bF29vbqH3Xrl2NRuoaNWqEnZ2d0euV+fVPSUnhxo0b1KhRAwcHh2z/v44ZM8bo/0urVq3QNI0xY8YYjpmamtK8eXOj69wrr+//7KxatQp7e3sef/xxo98zPj4+2NjYsG3bNqP29erVo02bNkZ9BvD19aVq1apZjmfX7/Hjxxv1bfz48SQnJ7N169Z89Wnr1q0kJyczYcIEo+eY3eh4Xt8XuRk9erTRSFr6ezb9OR4+fJgbN27wwgsvGK3Pevrpp6lQoYLRuRwcHAgNDeXQoUN5vn5ZIgFQGefk5ETXrl35/fffWbNmDWlpaQwaNCjbtpcvX8bNzQ1bW1uj43Xr1jXcn/6viYlJlimPzOsNAK5fv050dDSLFi3CycnJ6DZ69GgArl27lq/no9frmTNnDjVr1sTS0hJHR0ecnJwMUznpLl68SO3atXNdYHnx4kXc3NyoWLFivvpwP/d+6IBauzRx4kScnZ2xtrbGycnJ0C6939evXyc2NpYGDRrken4HBwf69u1rNM++dOlSqlSpgq+vb66PbdasGeXKlTN8cKcHQB06dODw4cPcuXPHcF/6+qWCyvxBAhh+ed67/uFeV65cYdSoUVSsWNGwrqdjx44ARj9jwLBu697rZL7G5cuXcXV1zTKdd+/7NSfpHxC7du0iISGBo0ePGl6zzK+jnZ0djRs3Nlwzp2vUqVPHcH9OLl++TM2aNbMcz+58I0aMIDExkbVr1wJw/vx5jhw5wrPPPpul7b0/E8j6eiUmJvLee+8Z1gGm/x+Ljo7O8vpnd057e3sAPDw8shzP7Wef1/d/di5cuEBMTAyVK1fO8rsmPj4+y++Z/PQZsr5nTUxMqFatmtGxWrVqARjWSuW1T+nvhXt/3k5OTlkCjvy8L3Jyv/+X6f2pUaOGUTszMzO8vLyMjr355pvY2NjQsmVLatasybhx49izZ0+e+1LayRqgh8Dw4cN54YUXiIiIoGfPnrmuPShK6QvznnnmmRzXWzRq1Chf5/z000959913ee6555g+fToVK1bExMSESZMmZbsQsLBy+qvz3kW2mWU3Hz9kyBD27t3L//73P5o0aYKNjQ16vZ4ePXoUqN8jRoxg1apV7N27l4YNG/LXX3/xyiuvGK1TyI65uTmtWrVi586dBAYGEhERQfv27XF2diYlJYUDBw6wa9cu6tSpkyWwyC9TU9Nsj2v3LBTNLC0tjccff5ybN2/y5ptvUqdOHcqXL09YWBijRo3K8lrldI2i5Obmhre3Nzt37sTLywtN02jTpg1OTk5MnDiRy5cvs2vXLtq2bXvf17841KtXDx8fH3777TdGjBjBb7/9hoWFBUOGDMnSNi8/kwkTJrB48WImTZpEmzZtsLe3R6fT8dRTT2X7Xs3pnNkdz+1nXxh6vZ7KlSsbjYpmdu97OT99hoL1O799elCK8jnWrVuX8+fPs379ejZt2sTq1av5+uuvee+99/jwww8L29USJwHQQ6B///68+OKL7N+/nxUrVuTYztPTk61btxIXF2c0CnTu3DnD/en/6vV6wyhLuvQpgnTpO8TS0tLo2rVrkTyXP/74g86dO/Pjjz8aHY+OjsbR0dHwffXq1Tlw4AApKSlGix8zq169Ov/++y83b97McRQo/a+je3cM3e8v+Mxu3bqFn58fH374Ie+9957h+IULF4zaOTk5YWdnx6lTp+57zh49euDk5MTSpUtp1aoVt2/fzvYv/uy0b9+ezz77jK1bt+Lo6EidOnXQ6XTUr1+fXbt2sWvXrjwlyCzMNGFOTp48yX///cfPP/9stFg2807G/PL09MTPz4/4+HijUaB736+5ad++PTt37sTb25smTZpga2tL48aNsbe3Z9OmTQQEBBj9wk//v3L+/Pkso3Lnz5833J9bn+99f+TW5xEjRjB58mTCw8P5/fff6d27d5bRg7z6448/GDlyJLNnzzYcu3PnTp52zRVGft7/96pevTpbt26lXbt2+VoQXFB6vZ5Lly4ZRn0A/vvvPwDDKEle+5T+Xrhw4YLRqNL169ezjDzl931REOn9CQwMpHPnzobjqampBAcHZ/mjtXz58gwdOpShQ4eSnJzMgAED+OSTT5g2bVqZT50hU2APARsbG7755hs++OAD+vbtm2O7Xr16kZaWxoIFC4yOz5kzB51OZ9hJlv7vvbvI5s6da/S9qakpAwcOZPXq1dn+Urt+/Xq+n4upqWmWv1RWrVqVZWvxwIEDiYqKyvJcIOMvnYEDB6JpWrZ/qaS3sbOzw9HR0WjnFMDXX3+drz5nPme6e18vExMT+vXrx99//23Yhp9dn0ANRw8bNoyVK1eyZMkSGjZsmOfRtPbt25OUlMTcuXN57LHHDIFM+/bt+fXXX7l69Wqe1v+UL1++yD8Us3utNE3jq6++KvA5e/XqRWpqKt98843hWFpaGvPnz8/zOdq3b09wcDArVqwwvDYmJia0bduWL7/8kpSUFKPXrHnz5lSuXJlvv/2WpKQkw/GNGzdy9uzZbHdM3tvn/fv3c/DgQcOx69ev5ziaMGzYMHQ6HRMnTuTSpUv3XQuWm+z+j82fPz/XUc+ikJ/3/72GDBlCWloa06dPz3JfampqsQRvmX+3aJrGggULMDc3p0uXLvnqU9euXTE3N2f+/PlGz/He3w+Q//dFQTRv3pxKlSrx/fffG+WQW7p0aZaA7N40HhYWFtSrVw9N07JNzVDWyAjQQyIvW3779u1L586defvttwkODqZx48Zs3ryZP//8k0mTJhnW/DRp0oRhw4bx9ddfExMTQ9u2bfHz88s2l8TMmTPZtm0brVq14oUXXqBevXrcvHmTgIAAtm7dys2bN/P1PPr06cNHH33E6NGjadu2LSdPnmTp0qVZ5uNHjBjBL7/8wuTJkzl48CDt27cnISGBrVu38sorr/Dkk0/SuXNnnn32WebNm8eFCxcM01G7du2ic+fOhkWOzz//PDNnzuT555+nefPm7Ny50/DXXl7Y2dnRoUMHPv/8c1JSUqhSpQqbN28mKCgoS9tPP/2UzZs307FjR8aOHUvdunUJDw9n1apV7N6922j6csSIEcybN49t27bdd2t5Zm3atMHMzIzz588btrADdOjQwRAk5CUA8vHxYevWrXz55ZeGaaL0RaMFVadOHapXr86UKVMICwvDzs6O1atX33fdUG769u1Lu3btmDp1KsHBwdSrV481a9Zku54lJ+mvx/nz5/n0008Nxzt06MDGjRuxtLSkRYsWhuPm5uZ89tlnjB49mo4dOzJs2DDDNngvL68sKQzu9cYbb/Drr7/So0cPJk6caNju7OnpyYkTJ7K0T89ftWrVKhwcHO4bYOWmT58+/Prrr9jb21OvXj327dvH1q1bDdvki1N+3v+ZdezYkRdffJEZM2Zw7NgxunXrhrm5ORcuXGDVqlV89dVXOa59LAgrKys2bdrEyJEjadWqFRs3buSff/7hrbfeMkxt5bVP6bmrZsyYQZ8+fejVqxdHjx5l48aNRqPakP/3RUFYWFjwwQcfMGHCBHx9fRkyZAjBwcEsWbKE6tWrG438duvWDRcXF9q1a4ezszNnz55lwYIF9O7dO8ta0jLpge03E0Um8zb43Ny7DV7TNC0uLk577bXXNDc3N83c3FyrWbOmNmvWrCxbUBMTE7VXX31Vq1Spkla+fHmtb9++WkhISJZtrZqmaZGRkdq4ceM0Dw8PzdzcXHNxcdG6dOmiLVq0yNAmP9vgX3/9dc3V1VWztrbW2rVrp+3bty/Llk9NU9t53377bc3b29tw3UGDBmkXL140tElNTdVmzZql1alTR7OwsNCcnJy0nj17akeOHDE6z5gxYzR7e3vN1tZWGzJkiHbt2rUct8Gnb4PNLDQ0VOvfv7/m4OCg2dvba4MHD9auXr2a7et1+fJlbcSIEZqTk5NmaWmpVatWTRs3bpzR1tV09evX10xMTLTQ0NBcX7d7tWjRIst22tDQUA3QPDw8srTPbhv8uXPntA4dOmjW1tZG23Vzeh3S35eZt+pm58yZM1rXrl01GxsbzdHRUXvhhRcM27Uzvz9GjhyplS9fPk99vXHjhvbss89qdnZ2mr29vfbss89qR48ezdN7Ll3lypU1QIuMjDQc2717twZo7du3z/YxK1as0Jo2bapZWlpqFStW1J5++uk8/6xOnDihdezYUbOystKqVKmiTZ8+Xfvxxx9zfA1XrlypAdrYsWOzPR+gjRs3LstxT09Po63Wt27d0kaPHq05OjpqNjY2Wvfu3bVz585laZfT75mcfv7Z/bwK+/6/16JFizQfHx/N2tpas7W11Ro2bKi98cYb2tWrV42e772/99L7cu/rk/57adasWVmex8WLF7Vu3bpp5cqV05ydnbX3338/S4qOvPYpLS1N+/DDDw2/1zp16qSdOnUqy2uuaXl/X+S0DX7VqlXZPsd7/x+kp8iwtLTUWrZsqe3Zs0fz8fHRevToYWjz3XffaR06dNAqVaqkWVpaatWrV9f+97//aTExMVleh7JIp2nFtGpNCFFoTZs2pWLFivj5+ZV0V0QJ+/PPP+nXrx87d+4sdAoDIe6l1+txcnJiwIABfP/99yXdnQdC1gAJUUodPnyYY8eO5ZpZVzw6vv/+e6pVq1bo9AVC3LlzJ8uaq19++YWbN29mKYXxMJM1QEKUMqdOneLIkSPMnj0bV1dXhg4dWtJdEiVo+fLlnDhxgn/++YevvvqqWHbniUfL/v37ee211xg8eDCVKlUiICCAH3/8kQYNGjB48OCS7t4DIwGQEKXMH3/8wUcffUTt2rVZtmxZmd9qKgpn2LBh2NjYMGbMGEMNKyEKw8vLCw8PD+bNm2dIEzJixAhmzpz5SFWYlzVAQgghhHjkyBogIYQQQjxyJAASQgghxCNH1gBlQ6/Xc/XqVWxtbWXBoRBCCFFGaJpGXFwcbm5u963dJwFQNq5evZqlarAQQgghyoaQkBDc3d1zbSMBUDbSU3yHhIRgZ2dXwr0RQgghRF7Exsbi4eGRp1IdEgBlI33ay87OTgIgIYQQoozJy/KVUrEIeuHChXh5eWFlZUWrVq2MKuHe6/Tp0wwcOBAvLy90Ol22FXW/+eYbGjVqZAhg2rRpw8aNG4vxGQghhBCiLCnxAGjFihVMnjyZ999/n4CAABo3bkz37t25du1atu1v375NtWrVmDlzJi4uLtm2cXd3Z+bMmRw5coTDhw/j6+vLk08+yenTp4vzqQghhBCijCjxRIitWrWiRYsWLFiwAFA7sDw8PJgwYQJTp07N9bFeXl5MmjSJSZMm3fc6FStWZNasWYwZM+a+bWNjY7G3tycmJkamwIQQQogyIj+f3yU6ApScnMyRI0fo2rWr4ZiJiQldu3Zl3759RXKNtLQ0li9fTkJCAm3atMm2TVJSErGxsUY3IYQQQjy8SjQAioqKIi0tDWdnZ6Pjzs7OREREFOrcJ0+exMbGBktLS1566SXWrl1LvXr1sm07Y8YM7O3tDTfZAi+EEEI83Ep8DVBxqV27NseOHePAgQO8/PLLjBw5kjNnzmTbdtq0acTExBhuISEhD7i3QgghhHiQSnQbvKOjI6ampkRGRhodj4yMzHGBc15ZWFhQo0YNAHx8fDh06BBfffUV3333XZa2lpaWWFpaFup6QgghhCg7SnQEyMLCAh8fH/z8/AzH9Ho9fn5+Oa7XKSi9Xk9SUlKRnlMIIYQQZVOJJ0KcPHkyI0eOpHnz5rRs2ZK5c+eSkJDA6NGjARgxYgRVqlRhxowZgFo4nT6VlZycTFhYGMeOHcPGxsYw4jNt2jR69uxJ1apViYuL4/fff2f79u38+++/JfMkhRBCCFGqlHgANHToUK5fv857771HREQETZo0YdOmTYaF0VeuXDEqaHb16lWaNm1q+P6LL77giy++oGPHjmzfvh2Aa9euMWLECMLDw7G3t6dRo0b8+++/PP744w/0uQkhhBCidCrxPEClkeQBEkIIIcqeMpMHSAghhBCiJEgAJIQQQohHjgRAQgghhCh2QUFQmuqSl/giaCGEEEI8nEJCYOVKWLECDh2CChUgIgIsLEq6ZxIACSGEEKIIhYfDH3+ooGfPnozjJibQtClcuwbu7iXXv3QSAAkhhBCiyCxcCJ98or7W6eCxx2DoUBg0CO4p/VmiZA2QEEIIIfLt5k348Ufo1g3Wr884/tRT0Lo1zJkDV65o/Pb3Fap0Wce3Zz9Er+lLrsP3kBEgIYQQQuRJTAz8+aea3tqyBVJS1HFHR+jTB0JjQzmj20vH6QFsCA/g498DuJF4w/D4pxo8RW3H2iXUe2MSAAkhhBAiV4mJMGwYbNoEmctqetS6xYjhlox6uhwAi44sYvrO6UaPNTMxo0HlBjRzaYaJrvRMPEkAJIQQQggjt2/D8ePQpg0kpyVzLvo0R85UJSmpEtauQaTUXUpq3aWEOJ2jzbD11KjRG4BWVVrRskpLmrk0o5mrujWo3ABLM0t14lJUfEICICGEEEJw5w78uT6JH3+LZ8/WCmh6E65dgzUXlzNy3Uho1wG63iSx8inQQXnz8jRxaYepianhHL1r9aZ3rd6QlgaXLsHhs3B2K5w5A2fPQmBgqdkHLwGQEEII8QiKS4rj0JXjLP/rBn7rHQne3wj9HVtAjdZ4ecHFi9DMtRkOVg4062xGM5ceNHN9i6auTalZsSamqWlw4YIKeEzvBkJvvglffWU8V5ZZYCDUq/dAnmNuJAASQgghHnI3E2+SlJqEq60rAEeuHqHF9y3Q9r4Gm2dnNLQNxbrJep5/1oavnn8GnQ40rT43x4eiO39ejeTsOAVnVmaM6KTdDYJq1Lh7DlsV/FhZQZ06ULeuCnjS/61ZswRegawkABJCCCEeIpHxkQSEB6hbhPo3+OYV+ll/icvliXTrBt1610Gn0+HSch83D96kcedABgxKYfhjFXAPbYTu7Fm4EQWOjuh0OvjsM5g+PfsL2tnB1asZAdDzz8Pw4eDpmTEqVApJACSEEEKUQZqmkZCSgI2FDaCmtOosrMPVuKuqgV4HoW3g1GtwZjDr4tXoT0gI9O9fnusDD1Bxx0H0T3+AydnTMOWsSuOcrkoV6NFDfV2vHjg5ZR3NqVsX3NxUxsN0Li4P4ukXmgRAQgghRCmnaRqXbl3KMrLT3K05G59WFUZtLW3RoQO9CRX3LOLOsX7cvlHJcA4HiwQG9Ehk+KuOAFQ8fBrGjcuaEblKFRXcWFllHBsyRGU4fIhIACSEEEKUIpqmqWmnu/ot78f24O3EJMVkaXsi8gSaphYr16gBW1rOx+OHVfQ84MPu+ErYEks/1jGUFTyevAWLbnOgyzj14KZNVfbCzKM5deqAvX3WTpkUQf6elBS4ckWtGwoMhOrVM0aYSoAEQEIIIUQJSUlL4cz1M0YjO7dTbnP0xaOGNtF3oolJisHC1IJGdjXxwZVmt6ywC3Dl2H4fan8ew6Uoe8LDoa6uMvy8jHeJ4jbl6GG6FataVe8GOG9Cy5YZF2/UCP7+u2if0J07EBRkCHISL54nNPQ0odcvEZIYTqiNRogdhNpBx7NNmSIBkBBCCPHomLFrBmvOreFE5AmS05Kz3B8bcRk7F08APq/2IpbfBlMvIIRLKSmsoA1fMZQz1De0t7KCgADo3roBTJ9Ot/QRnRqrwNy8aDufkAAXL5L432nCAo8SEnqG0KhLhCRcJVSLMQQ4IfZwwwlwyv40luYlWxdMAiAhhBCiiMUlxXE88rhhZOfktZPsG7MPC1OVAPDirYscvnoYAHudNc1SHGl23Yxm/8XR7GQUNolfw8zPAGhZqzMcuMxyhjKM5YZrWJim0qN5FEOfsaDvyIrY2gLYwzvvFLr/d6IiCD29j9CLdwOcm0GExF8lNO0WIZZJhNpBVPm7jXMJcgDK6SzwsHLGo4IX7k7V8LCvirudOw0rNyx0PwtDAiAhhBCiCPx9/m+WnVpGQHgA/934Dw3jsg9ntq2kSddnAHje/Ql6TPuRZuHgfSsRHSFGba+cvc3KL8DbGwYOcIYtW+hSuT5WrTQ6d9YxdCg8+aQZDg7533GVlJpEaEwIoSGnCLl4lNCrZwm5EURoQjghabcINU/kerl7SlZUuHu7Rzm9KR46B9ytnfGo6IW7a208XGrjbu+Bh50H7nbuOFg5GK1pKi0kABJCCCHy6FrCNQLCAzgafpSAiABmPT4LLwcvAE5FnGDZqWWGtlXuWNDsqkazKyk0C4dqp1fB3QCoddO+cMVGLThuXg/q1eOqqw+rwtqyYn9V9v1lDn9B+/YwcKAOunbFCbh+HWxscu5fUmoSYXFhhMSEEBpzhZCwM4SGnSPkZjCht8MJ0Udz3SybDM064J7zWqeAR7IV7iYOeJRzvRvg1MGjWhPc3ergYedRaoObvJAASAghhMjBmetnWHl6pWEqKywuzOj+IWEOeE36HoAeNbqjffA+zULTaBoOzgl31/bodGoop7JHxgN1OoiMhHLl+P57+O032DUvo1aoTgcdOqgK7Jqmvk9KTeJ6ylUCLocQGht6N8gJISTyP0JuBhGaGMk1fVzOT8bk7g2wSgGPWPBIssTdpAIe5Vxwr+SNh1sd3Ks1waNuayo4epTZ4CYvJAASQgjxSNM0jaDoIEOQM7DuQHzcfAAVAH2440NDW50GtaOgWbi6NTXZA5PUfU2rNKdpxSehkg6G3V2EXLcu1K4N1taGc8TE3N1pXq4cAOvWwc49yWAfRoO2oTTtHELV+qHEaCFsigvl+0VXCI2+QuSdqDw9n/Tgxj0WPGJ1uJvY41HeDfeKXnhUqYt79aZUrN0UXbVqxrl+HjESAAkhhHik3Ey8ycYLG9VUVthhAsIDiEmNN9xvt3o9Pl+fAKBllZaMumRPs3MxNAuHxpFgY3K3xlW9eiqXTmarV2e5XnJaMueuBLPsnxA27A7lVEgII8aHEq2pkZyL7UKgZSQAp4BTCcDB7PtumQoeMenBzd1/E0xxL+eCR6VqeLjXo2K1Bugeq6ESA3l6Fv0usIeEBEBCCCEeSilpKZyNOktAeADVrd1oX7sbAFdirvDM2meM2lqkQsNrd0d2ggIN805V7auyuMXH0CAxI1lgphpXKWkpXI2+TEhspmmp2FBCYkO4fCuUi1EhxKRGqqEjgFrqtuRs1v5apt4NaO4NcGLAPdkKD8dqVKpaG131GtDgboBTo4bK3FyKa26VVhIACSGEKPNS9akcvRrA0Qs7Cbiwk4DrJziREkqSLg2AMactab8iEXQ66jnVo31cRRqfuUnTCGh2pwL1nBtgUac+PH43yNE0UvSpXI27SugTTe8GOGcJOb+Z0MMZgU5EfESW3V5Z6FSAdW9QYxTgxIKjuR26mrVUhuR6mQKc6tVVfa2HeD1OSZAASAghRJkSnxTH8ROb0YeE0H7AJAASkhNo+WMr44Y6sLujRnUahCRBVBQ4OWFhaoH/wL+42j2cUJdynNfF4Xd31CYkdjuhwb8RMickb8ENYKE3wS3BHI/oVKreSst2FMcpQW20wskpI7DpcDe4Sf++YkUJch4gCYCEEEKUWtGBpzh6YB0BwfsIuHWWAMI5b3MHTQcdg2G77yhwcMDeyp42qa7YBofTJNEeL1sPHF28SatShbDG5lwekMagbS8bpqci4iPQa/fPRGyOKe56Gzxum+N+MxWP8ATcb6QYBTiOt/WYaJm2llepAjVqEO9dnfKNa6CrmWkkx86u2F4rkT8SAAkhhChZSUlw4QLXTx4g5L+DNJvyJZRXaYbr/NKSSNNE1c424yGOCZBa3povd35GiMUdQuNC0Vdx47SDjq0JEei1U8ApuIG6ZcPcxJwq5hXxSLPB/bYZHjdScQ+Px+PSDRXsxIDT7TRMNOMipHqdCSG6qvynr8FBahBIDWIqVadOnxpM/Koa5vZqd1cu6XpEKSABkBBCiAdK27uXq+uXERB2iID4QAIsbxDgAqH24BIHa/e1IcS1PKGxoVhZlKNcUhJWOjNSTXXEk4wejajyEFU+kT1HZ2Z7DXMTc6rYuuFuXkkFOAmmKsC5GodH4HXcz4dTOS4FEy0SiMzmBObg7Y1WvQbUyJimem1hDRZu8CJFs8DVFQYPhqFDoXXroimYLh4cCYCEEEIUrVu34OxZOHsW7ewZwgIDcP10ARHuDoTEhjDObwgBlmFQLetDI2yhzZ7RxgfN4DYZBUPNTMyoYlsFdzt3PMq54p5WDo8EMzU1FRqHR+A1Kp8LwSTkCmiXc+6nlZXxGpy7N713dfZc8WDFajP++AO2fg4NGqiH9DSDJE8V9Dz2mGy+KsskABJCCJF/mqZuJiak6dOI3LCSyz98waH4Cxy2jeOsE1yxhxvlIK0JmK5qRBr3X3NjqjOlil0VQx0pDzsPNYoTb4J7VDIeoXFUvhSBaeAlCAyEiD25n9DW1jjAyRzwuLoahm00Dfbvh5UrYdUqCMuU8Hn16owAqFs3dRNlX6kIgBYuXMisWbOIiIigcePGzJ8/n5YtW2bb9vTp07z33nscOXKEy5cvM2fOHCZNmmTUZsaMGaxZs4Zz585hbW1N27Zt+eyzz6hdu/YDeDZCCPHwSEtLJfLCUUJP7yPkYgCh4ecJib7CleTrhDWpRqh5IlfjrpKqT4WmuZwHvSG4cSnvQlX7qng6eN4NdKqoaarrSTiHRmN68RIcClQBzsWtcCOHRTzpKlXKGtykf+/kdN+dVefPq6DmypWMY3Z20L+/Gunp2jUfL5goM0o8AFqxYgWTJ0/m22+/pVWrVsydO5fu3btz/vx5KleunKX97du3qVatGoMHD+a1117L9pw7duxg3LhxtGjRgtTUVN566y26devGmTNnKH93YZ0QQjzq9JqeyPhItTMq+jIhl44RmnaTkNSb6ljkBa4mXiM18zSPw90bwO3zhsMmmKBHjwk6KlhVwN3eg7qOdWnm1ow27m3wtvPEJU7D9FKQCmyOBsLFixDor76Py6WGFag8ONmN5FSvDhWyKVOeA02DkychIiJjJMfbW13exgaeeEIFPd27g6Vlnk8rcpCaCqGhEBwMQUHqlv51t27w7rsl1zedpmn3T3JQjFq1akWLFi1YsGABAHq9Hg8PDyZMmMDUqVNzfayXlxeTJk3KMgJ0r+vXr1O5cmV27NhBhw4d7tun2NhY7O3tiYmJwU62LAohyiC9pudawjWjzMTpgU5oZCAhsSGEpdwgNQ/TUmjcTWJjbHyzl3i66Ug87FSRz5jbN6mdYK2CnIsXVWCTfrt4Ee7cyfkaOh14eGQ/klOtWu4l0PPg7FlYsULdzp1TQc/FixmDQ0eOqETPmUp2iTzQ6yE8PPsAJygIQkIgLS37xz75pKqDVpTy8/ldoiNAycnJHDlyhGnTphmOmZiY0LVrV/bt21dk14mJUVsYK1asWGTnFEKIkpIe3NxbeiHzv2GxYaToU+57LhM92CaDeRokmUFni1p07P4iHnYeXLkVxBS/N0EHlawr0cy1GS0cG9NOX4Um8Ta4XErAZMvvhmCnSlAQpORyTVNT8PLKsuiY6tVVRFLEhTkvXoTly1XQc/JkxnFLS2jcGGJj7xYlBXx8ivTSDw1Ng+vXcw5wLl+G5OTcz2FhoaqHeHurH7+3t7rVq/cAnkAuSjQAioqKIi0tDWdnZ6Pjzs7OnDt3rkiuodfrmTRpEu3ataNB+iq2eyQlJZGUlJHEKjY2tkiuLYQQ+aXX9FxPuG4c1MSEEBqn/g2JDclXcOMaD+5WlfFo0gF3W3csNB07180jpIIJYdYp6E0gJlPc0bjDUCY3fgkuXSI+LInut0fhGZWKzZUIdIEX4Iqf+rM/JxYWxiM4mb+uWvWBFub8/HNYtEh9bW6uplyGDlUjDzK4n+HWrewDnOBgdUtIyP3xpqZq8O7eACf960xrzUuVEl8DVNzGjRvHqVOn2L17d45tZsyYwYcffvgAeyWEeBRpmsb129dzHLUJiQkhLC6M5LT7/EkN6NDhauuqdkpZu+Dx61+43y27YJUKN60h2M2a497l6Fu5E2MGrwDgfNR5Pj84B9s7aTQJh7ZJTrS5U5kGsZZ4RKXgsOgHuDodUIn8sv2zsXz5nBcdl0BhztBQtXNrxQqYMwfatFHHhw1TH+BDh6oFzflYKvRQiY83DmruDXRiYnJ/vE4Hbm45Bzju7mBWBqOJEu2yo6MjpqamREYaJ6GKjIzExcWl0OcfP34869evZ+fOnbi7u+fYbtq0aUyePNnwfWxsLB4eHoW+vhDi0aFpGlG3o1Q9qRwCnNDY0DwHNy42LnjcTMX9lh6Pq/G4X08yKr/g2rIz5pv9AIi5E8Os/W3xs08gwPIWkfpY0KBiYiI1biZyJzwEPvoIAgOpFRhIwtnylItO/7P++t3bPRwcct4+7uxc4jWrIiLgjz9U0JP579vlyzMCoE6d1O1hd+eOmorKKcCJirr/OSpXNg5qMgc6Vas+nAvCSzQAsrCwwMfHBz8/P/r16weoKSs/Pz/Gjx9f4PNqmsaECRNYu3Yt27dvx9vbO9f2lpaWWD6MP10hRJFID25ympZKD26S0pLue6704MbdwlFt/Y7X4XEtCffLt/Co6IX7nJ9ws3XD3NRcbeGOUlvA9ToIbOxBQCdH/qxqThUPTyZqGkREYHX+DFdDz9L2uMaIm1D9FtS+ZYJdYvpU1b67N7WWuVx6ZypXzn4kJ70wZyl0/boa0dmxw3gmrl07dXzQoJLrW3FJSVGLiXMKcMLD73+OChWyH73x9lbrcx7FDdIlPmg1efJkRo4cSfPmzWnZsiVz584lISGB0aNVJtARI0ZQpUoVZsyYAaiF02fOnDF8HRYWxrFjx7CxsaFGjRqAmvb6/fff+fPPP7G1tSUiIgIAe3t7rGWJvxAiE03TuJF4I9dpqbwGN4AKbtIT+JV3xcPEAXePBnjYq8R+bk8+g8WBw5CUzaeWtwYOnoZ+/fpuX46mXSUkLozEy4FUuRZC9UshtDoMjeJOwusrISEBS+CnLCe7Gx3cLcyZZTSnjBTmvHVL7dpKH9WpVAn++08FP61aqaBn8GA1DVNWpaXB1as5BzihobkvuwIVwKQHNNlNVaUv9hYZSnwbPMCCBQsMiRCbNGnCvHnzaNWqFQCdOnXCy8uLJUuWABAcHJztiE7Hjh3Zvn07ALochmYXL17MqFGj7tsf2QYvxMMnTZ/G8cjj+Af5cyLyhFGQcyc1l+3ZmTiXdzYEMkaZii0q4XEtCbegKCzOXYAzZ9S+64sXVcCReVNH69Zw4IDab12nDkn1anG6pgOB5nGYJ6fR36m9Yet44MF/cb+RglUO24gBtbrU0zPn7eNl8I++2Fj48081vbV5sxq9CAvLWGfi76+empdXiXYzzzQNIiOzLjBOD3SuXMl98xyoKaicRnC8vFRgWMKzkqVCfj6/S0UAVNpIACRE2adpGueizuEX5Id/kD/bg7dz686tHNs7l3dWAY29B+627lkCnSp2VbCITVCBTXg4DByY8WAfHwgIyP7EFSuqT7/UVAgKInDdT0QEnSQ57ApWwaFUjojD6xaY5fKbOM3MlFSvqpjXrINJzZrGIzleXmrnVRkXHw/r16ugZ+NGVSA+XcOG6r6qVUuuf7nRNLh5M+cAJzg49xRIoIK7qlWzBjjp/zo7l86dVKVNmckDJIQQRSnoVhD+Qf74B/vjH+RPRHyE0f22FrZ09OpIG/c2eNp7GoKcKrZVsDS7Zx3gwYOw8xCc3ZsxonN3Oh0LC7U3OH1Iom5dtUijVi2159fOjkRSuXUzDC00lCrVqql5DE2jBlAjm77fsTDhlltFKjdug2nN2kajOaYeHpg+5FU3p09X29bT1amjpreGDlUvb0mLjc05F05w8P0TWet0apoup51UJbB57pEnAZAQosy6GneVbUHbDEFPcHSw0f1WZlY8VvUxfL188fX2xcfNBzOTu7/2NE0FLQfOwNk/1cKShQsz/sz+4gu1t/pebm4q6cnixWpk5+JFUi5eQE8alnsyCnNa370ZsbUlwtWWCxX06KtVw7ZeU9yadsC5STus3NxwfQTmMJKS4N9/1UjPmDHg66uODx6sio6mBz0NGz7YKZ3btzN2UmUX4Ny8ef9zuLjkHOB4eDwUA3UPFQmAhBBlxo3bN9gevN0Q8JyLMk6YamZiRqsqrfD19qWLdxdau7fOOrLz3Xfwww9qROfeDG9Tp6p5iOvX1Z/rTZpAuXJqBWpcnJr6unpV3Q4cMDwsc2q/KGsIrKhu193s0FWvwZAn3sKtWQdwdMRFp6PwST7KlpQU2LpVBT3r1mXknbGwyAiAfHzgwoXiC3qSk9Vam+yS/QUFqVj2fipVynmruKdnmVxu9UiTAEgIUWrFJcWx68ouFfAE+XMs4hgaGYtldOho5toMX281wvNY1cewscilZtTixfDSSxnfm5qqreB2dmrkp08fNQxwn/mMqzZgW78JtvWaQvXq+JteYd6NDVRo0Jw61VvTzLUZPVyb4ljOsbAvQZmWmAivvgpr1hiPoLi5wZAhMHx4xrHCBj6pqWqhdE4BTliYGvTLja1t7jupbG0L10dRukgAJIQoNRJTEtkXus8Q8BwMO0iaZrwFqr5TfUPA09GzIxWs75PeNy1N7cI6dAg2bVI1ETRNfWKmpalRnXsTqeh0xDo7cMj6lmE0J7AiXKwAQZV0eLrVY17P2fh6q+GLzpqG7yMwfXU/aWlqFKdOHfW9lRXs2qWCn8qV1TTX0KEqZ09+F/Tq9WoJVk5bxUNC1I80N9bWue+kqlBBdlI9SiQAEkKUmJS0FA5dPWQIePaG7M2Sb6d6heqGgKeTVydcbHKZQEpf13PwoLodOgSHD6stRvfQm5pww9mWixVNOG4Tz1n7FPr1eZ1OXZ8Hb2+OXN1Lz9+606ByA5q5NuNx12a86dqMRs6NKGdezuhcOaXeeBTo9bB/v5reWrVKDZ5du6aCDZ0OZs1SOWo6dsx9ka+mqYzFOQU4ly8b7wzLjrl5RtHN7AKcypUlwBEZJAASQjwwek3P8YjjhjU8Oy/vJD7ZODhxs3VTAc/dhcuedxMDZuvGDRXkHDqUEfRcu5a1nbU1tGhBanMfPrmzmV9NT3PZQU+qaUYRJCszK7ybe9Dp7vDFY1UfI25aXNY1RAJNU3HlihWwcqWKOdM5OKhNc+nV1fv2zbgvOjrnACcvRTdNTDKKbma3FsfNTbaKi7yTAEgIUWzSc/GkBzzbg7dzM9F4O00l60p09u5sCHhqVaqV/YjK7dsq1056sHPokEo0eC8zM2jUCFq2VFuJ5s2DS5fg9dc50KQSM375GrCkTZWWNHNtZrjVcayTsUMMVCkKka2vvoLXXsv43tZWVVh/8km1ez8sDObPzxrgREff/9z3K7r5AIvJi4ecBEBCiCKV11w86QFPQ+eGmOju+bM9NRVOnzaeyjp1Si0yuVetWirYadFC/dukiVp8kpoKTzwB58+ruY+GDWlX1Zu9Y/ZyK/EWXap1Kb4X4SFy+rQa6WnXTk1jXb6sAh5zcxXsVKyYsbX9t9/ufz4np5wDnKpV1Y9OiAdBAiAhRKGEx4WzLXibYR1PUHSQ0f1WZla082hnWMfT3K250UgLmqZGctIDnYMH1UhPYmLWi7m6qiAn/da8uZpzuZemwfjxKqWwtTX8/bf6hAWauTYrwmf/cElJUfkad+6EtWthz56MSuJWVlmzGZ87l/UcDg45BzienmCTyyY9IR4kCYCEEPlyM/FmRi6eIH/ORp01uj9zLh5fb19au7fGyizTn/WRkcbBzqFD2WeZs7dXAU7m0Z0qVfLWyVmzVL4fnY4bv37HwDNvMKvKLFpUaVGIZ172pW96yynZX0hIzkU304Of9KKb2eXC8fLKPh4VojSSAEgIkatC5eKJi4Pd+42nsq5cyXoRCwto2tQ42KlZs2ArWleuhDffBCBtzpc8nbiUHZd3MOrPUZx46QSmJg9vvQFNU2vAcwpwLl++f9HNdI6OaglVhw5Qr15GgOPoKDupxMNBAiAhhJE7qXfYF7LPsI7nYNhBUvXGCVbqOdUzrOHp6NWRitYVVardEydg8y8Zoztnz2bNPqfTqU/U9EAnfbFyUdUJ+Ocf9e/EiXzY6Cb/7vwXazNrlg1cVuaDH02DW7dyTvYXHJz9zGFmpqZqlCY1VcWn06apWlve3mq9j14PgwaprMdCPMwkABLiEZeSlsLhq4cNAc+eK3uy5OKpVqGaIeDp7N0Zl3KVVe2sQ4fg6/dVsHPsmAqC7uXpmRHstGih9kcXZ0rdxYuhSxf+bmHH9JX9AVjUdxGNnBsV3zWLUFxczgFOUFDeim5WqWI8LVWpknrsvn2qgseNGxlt27SB3r3V923bFuMTE6KUkQBIiEdMXnLxuNq40qVaF3y9VMDjlWCugpzfD8LBH1QSmNjYrCevWDFjVKdFC3Vzdi7+JxUbq1bXmpiAiQmBfdry7KLmAIxvMZ5nGj1T/H3Io8REFdDkVFk8L0U3nZ1zzoVTtarxYNrq1arsROa1PW3aqIzMgwerbedCPIokABLiIadpGudvnMc/yB+/IL9sc/FUtK5IZ6/OdPHugm8lH2oFRqM7dAh+/hMOvp21VASo3VU+PsZTWd7eD36BSGIi9OihPvmXLCHBJI0BKwYQkxRDW4+2zO4++4F2J73oZk4BTl6KblasmPtOqnLlsn9cTAwsW6aqknfvro61aaOmzpo3V0HPkCHqpRLiUScBkBAPoeDoYMOiZf8gf8LjjQMYGwsbOnp2xNe9Pb6JLjQ6dwuTvw7DwTmqmNO9TE2hQQPj0Z369VXSwZKk18PIkWpu5+xZCAkh1aMyng6eXEu4xqrBq7AwLaK1RXelpamt4jllMw4Ly3knVbr0opvZBTheXqo2a17Fxald/itWqFJnycnQtWtGAOTmpvorIz1CGJMASIiHQJ5y8bi3xbd8A3yv2+Bz/BrmfwTAiXeyryBZvbpxsNO0ac7DDiVp2jRVgMrcXJUcr1kTe+DPp/7kSswV3Gzz/6mfXnQzpwDnypW8F93MKcCpWLHwA2Vr1sDvv6s135nz89SrB506qVGf9GtI8CNEVhIACVEG3Uy8yY7gHfgF+eWci8exCb56T3yvmNL64FWsDh2ABP+sJ3N2Nt5+3rx52dgC9O238Pnn6usffyS8eR1cNA2dToeJzgQvB69sH5ZedDO7Bcbpa3PyWnQzp1w4zs5FPxOYkmJcBuLrr8HPT31ds6aa3ho6VA3UCSHuTwIgIcqA+OR4dl3eZVi4fDT8aJZcPE3LVcP3tjO+55N5bPslbMMPA4eNT2Rjk5FcMD3o8fAoe4ldNmyAcePU1x9+SHi/rjRb1IzHqz3Ot32+zVKtHdTU1cqVMGMGnDyZ++nTi27mFOC4ueVe2byoJCfD5s1qemv9erVNPX0056WXMtb1NGlS9n6EQpQ0CYCEKIXylIvHxBnfKFt8j8fS8eA1KiZeBDIVBzU3h8aNjUd3atd+MJ/cxSk+HkaMUHNVo0aR8tZUhv7alYj4CALCA9BrxgtwkpPh119h5kwIDMw47uqadXoq/WsPj5IrupmSAv7+KuhZu9a4gOjff8OLL6qvBw1SNyFEwUgAJEQpkKpP5VDYodxz8aTY4htqhm9ANJ2CNFzjI4FMW4rq1DEOdho3BkvLB/tEHgQbGxUZzJ8P333Hm35T2XVlF7YWtqwZusaQhToxEX78Uc2ShYSoh1asCJMmqcGjihVL7inkZN8+6Ns3I08PqB1dQ4aokZ7WrUuub0I8bCQAEqIE6DU9JyJPGBYt77y8k7hk4wx3rnfM8Q1Mw/eiHt8g8IrOdL+7Ozyeafu5j4+qnfWoaN8e2rdn+anlzNk/B4Cf+/1MrUq1iIuDb76BL7/M2HLu4gJTpqjRk9JSjFOvh7171QiVr686Vreu2tXl5AQDB6qgp337sj9oJ0RpJAGQEA9A5lw8/kH+bAveljUXT6KOzkEavkHgGwS1o1LQgapbkDmTcosWj962npQUGDsWJk9WZTOA09dOM+avMQBMbTeVjs79+fBD+OorVS4CVL6bN9+E555T1cxLmqapfJIrVqjNa6Gh6sd64IC638EB9u9XT7GkMwwI8bCT/2JCFJPL0ZcNu7T8L/kRnhBhdL9NEnS8jCHgaRSpYWJhCc2awfBMozs1ajzaK1w1DV5+GZYsUSuCL14k1cKMwasGczvlNu0rDSJ18yd49lfLg0Dtipo2DZ55puTW8mR27JhKULhypdplls7OTo36ZN7h1bRpSfRQiEePBEBCFJGI+Ai2BW3D/+IW/P/bzKXEMKP7LVOh3ZWMgKd5hA7zug3UiM7Iu6M7DRuWjk/s0uTTT9ViHhMT+O47sLLCDHi38fdMfDeUQ/uHsOuOChAbNYK33lKLg0vTtNFHH6llSwDly8MTT6jpre7dS8fIlBCPIgmAhCigm4k32RG0Df+ja/C/vJ0zKVeN7jdLg5ZhGQFPG9OqWPm0hifvBjvNmpWeBSml1dKl8M476uv586FPHwID1Y6uX35pR0qKuqtlS9WsT5+SHSw7d05Nb61cqQKeWrXU8WefVQHZ0KHQq1fpzCkpxKNGAiAh8ig+KY5dR9aogOfaAY7qItEyfdjqNGgafjfguWHHY26tsfVpAwPvBjxOTiXX+bJoxw61eAfg9dc51eEVXnvyGv7rndDr1QvfqRO8/TZ06VJygc/FiyroWbECTpzIOL5yZUbs1r+/ugkhSg8JgITIwZ1rV9m//Tf8z27AP/4EB6xvkZo+rWKi/ql7HXxDzPA1r0VHr05U6tJRBTteXo/2up3C+u8/FTEkJ3PY9w0+CZzJuoYAlQFo5xvN59MdaNu25LoYGAhPPQVHjmQcMzODbt3USM+TT5Zc34QQ9ycBkBAAt2+TeuQQhw+sxT94G/6pF9jjmMid9OU4d2eqvG+Bb2wlfO0a07l+b1wHPK5WscqWnaLl7MxO75F8oh/KZv+7yW90eqizhroD1rL1gx+xesAveViYyieUnounShU4f15Nbfn6qqCnf//SmV9ICJGV/NYWj57UVDh9Gv3BA5w49i/+kfvxt7zKTk+IswQyzVS53jbFN9UDX+fWdPYZhHfbXqrSpSgWmqY2en3yiT27AlR+H1NT8G6/n8B6z1HRM5INY49gZfZgVg5HRsIff6jprd27VSLtM2fU4J61NaxbpxZey+ymEGWPBEDi4aZpqsrlwYNoBw/w3+md+MedxN89hW3ecKMy6bMqAFRINaezeU18vXzp0u4ZaldriU6msoqdXg9/rk3jkzfjOHLRAQALCxg1Crx7r2Ta0aHo0LFs4KYci5wWlagoVWl9xQrYvl31LV2lShATo/L1gFp7JIQomyQAEg+XyEg4dEjdDh7k8rn9+DtE4+8N/t5w9Z41IzaaBR3sGuBbtxe+jfvT2LUJJjqTkun7Iyg1VS0W/vRTOH3aFHDA2iyZF8dbMGUKhOsO89hPIwCY3nk63ap3K/Y+/e9/KuVQupYt1fTW4MGqRpgQ4uEgAZAou+Li1ArUu8EOhw4RceMy27xUsONfCy7dUzvJUmdOOycffOv1xrdaF5q7NcfcVPLuPGjJyfDLL2o7+8W79VvtiGEcC5k0vw6VXxoAwJTVs0lKS6Jvrb5Maz+tSPsQGwt//aVGej78UGUlAFV36/hxFfQMGaIKpAohHj4lHgAtXLiQWbNmERERQePGjZk/fz4tW7bMtu3p06d57733OHLkCJcvX2bOnDlMmjTJqM3OnTuZNWsWR44cITw8nLVr19KvX7/ifyKieCUnw8mTKtC5G+xw5gy3LDV2eIGfN/j3hjOVjR9mqjOllVsLfKt1xdfblzYebR7Y+hGRVWIi/PADzJqVUaC0km0yk+KnM16bj8Onb8Ld4AdUfa+6jnV5tdWrRTYyt3u3qhO2YQMk3a03W7duRgDUowf07FkklxJClGIlGgCtWLGCyZMn8+2339KqVSvmzp1L9+7dOX/+PJUrV87S/vbt21SrVo3Bgwfz2muvZXvOhIQEGjduzHPPPceAAQOybSNKOb0eLlzICHQOHlS1BJKSiLeA3VXvjvC0gwBXjHPxoKOJSxN8vX3x9falfdX22FralthTEUpsbEaB0mvX1DEXF5gyNIQXF/lgo12H55+HqVONHmdhasF7Hd8rsn5s26YCnORk9X3t2mqkZ9iwjDay5EuIR4NO0zStpC7eqlUrWrRowYIFCwDQ6/V4eHgwYcIEpt7zi/BeXl5eTJo0KcsIUGY6na5AI0CxsbHY29sTExODnZ1dvh4rCiAszDjYOXxYrTQF7pjBfve7AU9NUw646Ek1MX7L1nWsawh4Onp2pFK5SiXxLEQ2bt6EefNUgdLoaHXM01MVKB3texmrjq3Uuq3u3eHvv8HcnD/P/cmuK7uY2XUmZiZF9zfa0aPQsaOaOe3dGz75RO3gkoBHiIdHfj6/S2wEKDk5mSNHjjBtWsa8vomJCV27dmXfvn0PtC9JSUkkpY+Fo15AUUyio1WAk3kq62pGCYlUEzjiCv5NzfBrWJ49FeO5o0u7e6/618vBiy7eXfD19qWzV2dcbV0f/PMQuYqIUKM933yTUaC0Vi1VoPTpp++WO/ttlxoOatRIrYQ2N+fCjQuMWDeC2KRYPO09mdBqQpH1aedOFfx06qS2tksNLiEebSUWAEVFRZGWloazs7PRcWdnZ86dO/dA+zJjxgw+/PDDB3rNR8KdO2rqKvPozn//GTXR6+Ckqw7/Ni741zRjR7nrxGl3gFRAjQK52LioER4vNcrjXUFWpZZWV66o9T0//KB+/KDim7ffhoED7ylQ+swzaj95kyZgZ0dCcgIDVg4gNimWdh7teKn5S0Xat4kTwdVVTYFJ8COEKPFF0KXBtGnTmDx5suH72NhYPGS/a/6kpcHZsxmBzsGDqjBSaqpRMw34r4k7/q2dVS4e3WVupMQA4YYGFawq0Nm7syHgqeNYR3LxlHIXLqQXKM34kbdqpWph9e6daZpJ0yAhIaMIbJ8+dw9rvPD3C5y6dgoXGxdWDV5VJLvzYmJU0JV+uSFDCn1KIcRDosQCIEdHR0xNTYmMjDQ6HhkZiYuLywPti6WlJZaWlg/0mmWapqk/9dMDnYMH1Xb0hISsbStX5vJjDfBvZId/pVj875zhakIoEKoGeQAbCxs6eHYwBDyNXRpLLp4y4tQplcNnxYqMhIGdO6sRH1/fbNbXTJ+uGv/zj6qXdte8A/NYdmoZZiZmrBy0skimNRMT4Ykn1EjUP/+Ao2OhTymEeIiUWABkYWGBj48Pfn5+hkXKer0ePz8/xo8fX1LdEtmJijLKtcPBg3D9etZ2Njbg40Nky/psq22Bf7lI/K8f5OItf3X/DfWPpaklbT3aGhYut3BrIbl4yphDh9Qi4j//zDjWq5cKfHIsUPrLL/D+++rr7dtVmmdg1+VdTNkyBYAvHv+C9p7tC92/1FS1s2vnTrCzU8vMJAASQmRWolNgkydPZuTIkTRv3pyWLVsyd+5cEhISGD16NAAjRoygSpUqzJgxA1ALp8+cOWP4OiwsjGPHjmFjY0ONGjUAiI+PJzAw0HCNoKAgjh07RsWKFalateoDfoZlUEICBAQYT2UFBWVtZ2YGjRtDy5bc8qnHDg89/qkX8A/exunrX0NoRlNTnSktq7Q0BDxt3NtgbS71tMqinTvh449hyxb1vU6n1va89RY0bZrLA/39YcwY9fXUqYbgB+DWnVtYmVnRp1YfXm31aqH7qGnw0ksqOLO0VMkOGzUq9GmFEA+ZEt0GD7BgwQJDIsQmTZowb948WrVqBUCnTp3w8vJiyd289MHBwXhnk5a1Y8eObN++HYDt27fTuXPnLG1GjhxpOM/9PDLb4FNS4PRp4x1Zp04ZFz9KV7u2qgnQogUJzRqyu0IcfmG78A/yJyA8AI2Mt5Hk4nm4aBr8+68a8dm9Wx0zNYXhw9Wurrp173OCM2fUsFBMjEq68/vvYGI8xXku6hwedh6Utyhf6P6+9RbMmKEusXo1SB5UIR4d+fn8LvEAqDR6KAMgTVM1BzIHOwEBGVt1MqtSBVq0UAFPy5YkNW7A/vhz+Af54x/sz/7Q/aTqjRc313GsowqIVusiuXgeEnq9GkX55BO1xAtUgdLRo+GNN6BatTycJCICWreGy5ehXTvYutWwBSs2KRY7y6L9/zV3LqTnSP3+e5VbUQjx6CgTeYBEMYuIMF6zc+gQ3LqVtZ29fUaw06IFtGhBqqszR64euRvwzGT3/t3cSTUOlDztPTNy8Xh3xs3W7QE9MVHcUlPVOuUZM9QAIYC1tZpWev11FR/n2bhxKvipWRPWrTMEP8tOLuO1f19j5eCVdPDsUCT9jo2Fzz5TX3/6qQQ/QojcSQD0MIiNVX+iZx7dSS+0lJmlpVqokR7stGwJNWqg18Gpa6fwu+SH/84f2RG8g7jkOKOHSi6eh19Sklqn/NlnmQqU2sH48TBpEjg5FeCkCxaodWULFhhWIZ+MPMnzfz/P7ZTb/Bv4b5EFQHZ2aopu5cosFTWEECILmQLLRqmeAktKUvl1Mgc7586pKa7MdDqoX99oKosGDcDCAk3TuHDzghrhCfJnW/A2om5HGT1ccvE8Om7fzihQGnp38XqlSmoqadw4lauwqETfiabF9y0IvBnI49UeZ+PTGzE1Mb3/A3ORlKRieyGEkCmwh4VeD+fPG09lHT+eUckxM0/PjECnRQtV2to2Y+HxlZgr+J/53RD0hMWFGT28vHl5lYvn7sLlxs6NC/3BJEq32Fj4+mtVsiI9q4GrK0yZAmPHZiQPzLf581UENXy40WG9pmfE2hEE3gykqn1Vfh/4e6HfY2fPQrdu8O23KuGiEELklQRApYWmqT+/M28/P3xYFS+6V6VKxtNYLVpA5cpGTSLjI9l26h9DwHPx1kWj+yUXz6Prxg1VoHTePOMCpem70wtVJmLtWlVzQtPUKunWrQ13zdg1g7//+xtLU0tWD1mNY7nCJeYJCVHBT2ioWq/Uq5cUNhVC5J0EQCXl5s2MoqDpQU9ERNZ25cqp0ZzMozve3ll+00ffiWZH8A7DTq1T104Z3W+qM6VFlRaGhcuSi+fRExEBs2erAqXpSbtr11Zb2YcPv1ugtDAOHFCVTjUNXn5Z1cK4a3vwdt7d9i4AC3stpLlb80Jd6saNjOCnTh21W02CHyFEfkgA9CDt3AnffaeCnUzJGg1MTaFhQ+Ngp149lXTwHgnJCey+stsQ8ASEB6DXjPP3NHFpYljD096zfZFvORZlw5Ur8Pnnap1PUpI61rixyto8YMA9BUoL6tIl6NtX1Z/o1UsNL2WKSJq7NWdI/SHYWtgyptmYQl0qIUFNd507B+7uKkdRJcm6IITIJwmAHqSrV1USuHQ1ahhPZTVpokZ8spGUmsT+0P2GgOdA6AFS9ClGbdJz8fh6+9LRq2OhpxhE2fbff6pA6a+/ZhQobd1aFSgt0umimzfVCa9fV7sMV6zIErTbWNiwbOCyLPmj8islBQYNUoNNFSuq4EcSvAshCkICoAepfXtVDLJlS2jeXP0Gz0GqPpWA8AD8g/zxC/Jjz5U9JKYmGrWRXDwiOydPqjw4K1dmJPX29VUjPp07F/FUUXKyGkY6f14Nx6xfb7R6euOFjfSo0QOdTodOpyv0OrNvv4VNm9TfCf/8owZIhRCiICQAepCqVFF/fmdDr+k5de2UYdHyjss7iE2KNWrjXN7ZsGjZ19sXbwdv2ZouDA4eVFmb//or41jv3irwadOmmC5qZqYyPB89Chs2gFtGEP5DwA+88PcLDK43mBWDVhTJe/WVV9TUV58+RuurhRAi3yQAKiF5ycXjYOVAZ6/OhoCnrmNdCXiEEU1TS8s++cS4QOmgQaomVpMmxdwBExN18XHjjIKfQ2GHGLdhHABNXZoW+n2raep5mZrCwoWFOpUQQgASAD1Q4XHhbL64Gf9gFfSExoYa3S+5eEReaZqaCvrkE9izRx0zNVWbsKZNUzujipWfnypwan13J2Gm4CfqdhQDVw4kOS2ZfnX6MfWxwqVl/uEH9Ry//z7b/QBCCFEg8uvkAVpzdg3jN443fG9haqFy8dxduNyiSgssTC1KsIeitNPrVUmtTz5RtWxBFSh97jlVoNT7QVQo2bJFLXpu0UKtQs6UcDNNn8aw1cMIiQ2hVqVaLHlySaFGf9atgxdfVM+7UycYObLw3RdCCJAA6IHqUq0Lrd1bGwKeth5tJRePyJPUVFi+XCX8O3NGHStXLqNAqduDWv9+8qSaX0tNherVs6SLfnfbu2y9tJVy5uVYM2QN9lb2Bb7Ujh3w1FMq+HnuORgxorCdF0KIDFILLBuluhaYeKSkFyidOVOl2gFV9HPCBFWg1PFBZjq4elUlNwwNhY4d1ehPpiJcV2KuUHN+TZLTklk2cBlPNXiqwJc6fhw6dFDlOp58Ev74Q6a/hBD3J7XAhCjjbt9Wa15mzYKwu2XbiqtAaZ7ExaktZempl9euzVKBtKp9VbaP3I5fkF+hgp9Ll6B7dxX8dOgAy5ZJ8COEKHrya0WIUiS7AqVubhkFSsuXL4FOpaaquahjx1TNuQ0boEKFbJu28WhDG4+C77lPTVVb3CMjVbbqv/7KWGcthBBFyaSkOyCEULWt3ntPFSWdNk0FP15eKvHfpUtq5KdEgh9QHThwQEUif/9ttNJa0zTe2PIGJyJPFMmlzMxgzhxVEWbjRrAv+BIiIYTIlawByoasARIPSni4Gu3JXKC0Th0VBA0bVgQFSovKhQuqfl3PnkaH5+6fy2v/voa9pT3Bk4JxsHIoksulpRVRjTIhxCMlP5/fMgIkRAm4fFmt5fH2hi++UMFPkyawahWcOqV2PJV48JMekQHUrJkl+Nl5eSdTNk8B4GPfjwsc/KSlwauvqtpl6ST4EUIUNwmAhHiA/vsPRo9WdXC//lrt8mrTRpXQCghQO8xLxYf/3r0qOvv332zvvhp3lSGrhpCmpfF0w6cZ12JcgS6jaaq8xfz50KUL3LlTmE4LIUTeSQAkxANw4oRaR1ynDixZohb7+vqCv7/Kcty7dxEXKS2MwEB44gm1EGnRoix3J6clM3jVYCITImlYuSHf9fmuwMkO33tPXUKng7lzwcqqkH0XQog8kl1gQhSjAwdU1ua//8441qePKlBaKot5RkWpqa4bN6B5c5WE6B5TNk9hb8he7C3tWTN0DeUtCrY6e/58+Phj9fU338DAgYXpuBBC5I8EQEIUMU1TWYw/+QS2blXHdDoYPFgVKG3cuGT7l6M7d6BfPzUC5OmporZ7tp4lpyVzNuosAL/2/5UaFWsU6FLLl8PEierr6dNVuQshhHiQJAASoohomtq6/cknagkNqPU8zzwDU6c+gAKlhaHXw6hRaj7O3l7l+nFxydLMwtSCTU9vwj/In8erP16gS+3YoRZ5axqMH69Gw4QQ4kGTAEiIQtLrVWLkTz6Bo0fVMUvLjAKlXl4l2r28WbwYVqxQW8/WroV69YzuTk5LNhTqNTUxLXDwAyrHT/PmapDpq69K0donIcQjRQIgIQoovUDpp5/CWTUrVDIFSovCiBGwf7+qPdG5s9Fdek3PwJUDqWJbha96fIWlmWUOJ8mbihXV1KCZGZjINgwhRAmRAEiIfEpKgp9/hs8+yyhQam+vCpROnPiAC5QWFXNzVXwsG5/s/IT1/63H0tSScS3G0dC5Yb5PHxoKW7aoFACgAkUhhChJEgAJkUe3b6st2198kVGg1NExo0BpmSvbcOwY/PqriuRyqDa6KXAT729/H4Bven9ToODn5k1V3PTMGZVbcfz4wnRaCCGKhgRAQtxHTIxKWjhnjnGB0v/9D154oQRrdBVGSIhKPnT1KtjawgcfZGkSdCuI4auHo6ExttlYRjcdne/L3L6ttv2fOaNes759i6DvQghRBCQAEiIHUVFqke78+SoIApUceepUGDlSLXQuk2JjM4KfevVg0qQsTRJTEhm4ciC37tyihVsL5vWcl+/LpKSorf/79oGDg0oq7elZ+O4LIURRkABIiHuEh8Ps2aoSe3o5rLp1MwqU5jBbVDakRyUnT6pt7hs2qOjkHuM3jOdoxFEcyzmyesjqfC981uthzBh1emtrVeqjQYMieg5CCFEEZA+GEHcFB6u6VN7eKgBKL1D6xx+qQOmzz5bx4Ce98NbmzWoV8t9/5zgk079ufypZV2L5wOV42Hvk+1JvvKGWF5maqgKv7doVtvNCCFG0yvKvcyGKxPnzMHMm/Pab2toO0LatStDXs+dDlKdm5kz44Qe193z5cpWMJwd9avUhaGIQtpa2BbqUs7N63X76Sc22CSFEaVMqRoAWLlyIl5cXVlZWtGrVioMHD+bY9vTp0wwcOBAvLy90Oh1z584t9DnFo+n4cRg6VE1vpRco7dIFtm2D3buhV6+HKPgBlYGwXDm1sCmb1cjXE64THB1s+L6gwQ+oBeKnTqn0QkIIURqVeAC0YsUKJk+ezPvvv09AQACNGzeme/fuXLt2Ldv2t2/fplq1asycOROXbFL1F+Sc4tFy4IAqdt6kCaxcqWaG+vZVeQC3boVOnR6ywCddnz5quCubfeip+lSG/jEUn0U+bA/eXqDT79ql1lenuyeZtBBClC5aCWvZsqU2btw4w/dpaWmam5ubNmPGjPs+1tPTU5szZ06RnlPTNC0mJkYDtJiYmDy1F6WfXq9p/v6a1qWLpqmQR9N0Ok0bOlTTjh8v6d4Vo/PnNe3Spfs2e3PLmxofoJX/pLx2+trpfF9m1y5Ns7LStKZNNe3GjYJ0VAghCi8/n98lOgKUnJzMkSNH6Nq1q+GYiYkJXbt2Zd++fQ/snElJScTGxhrdxMNB09ROpMceA19f8PNTC5lHjVLlK5Yvh0aNSrqXxeT6dbWIqXVrlfQwB2vOruGzPZ8B8NOTP1HPKX9DNydOqMGlO3fA3R3s7ArTaSGEeDBKNACKiooiLS0NZ2dno+POzs5EREQ8sHPOmDEDe3t7w83DI/+7XkTpoter3Vs+PmoR7t69Km/PK69AYKCq/Vm7dkn3shglJqp5vkuXVKbGHAqTnYs6x6h1owCY3HoyQ+oPyddlgoKgRw+VJ+mxx1RAWaZ3ygkhHhklvgaoNJg2bRoxMTGGW0hISEl3SRRQaqraft2ggUp3c/So+vx//XX1Yb1w4SOQjE+vh2eeUYuaKlSAjRuhcuUszeKT4xmwYgBxyXF09OzIZ49/lq/LXLsG3bqpvEkNG8Jff0mNLyFE2VGif6s5OjpiampKZGSk0fHIyMgcFzgXxzktLS2xLLNpfQWoAqVLlqiyVkFB6pi9Pbz6qipQWqlSiXbvwXrjDVizBiwsYN26HIe6Zuyawdmos7jZurFi0ArMTPL+6yA2Vs2uBQaClxds2qRiLSGEKCtKdATIwsICHx8f/Pz8DMf0ej1+fn60adOm1JxTlF4JCapGV7Vq8NJLKvhxcoJPP4XLl+Gjjx6x4GfhQpXFEdQ8X4cOOTZ9t+O7jG02llWDV+Fs45xju+xERKibk5MqcZHDDJsQQpRaJT5bP3nyZEaOHEnz5s1p2bIlc+fOJSEhgdGjVeHFESNGUKVKFWbMmAGoRc5nzpwxfB0WFsaxY8ewsbGhRo0aeTqnKPtiYtRn/Zw5qmYXQJUqGQVKH8mpmNRUlc0R4JNPYPjwXJtbmVnxXd/vCnSpWrVgzx64dUt9LYQQZc4D2JV2X/Pnz9eqVq2qWVhYaC1bttT2799vuK9jx47ayJEjDd8HBQVpQJZbx44d83zO+5Ft8KXX9eua9s47mmZvn7GdvVo1TVu0SNPu3Cnp3pUC8fGa9vXXat9/NkJjQrVPd36qpaal5vvUer3aVS+EEKVVfj6/dZqmaSUYf5VKsbGx2NvbExMTg53s6S0Vrl7NKFB6+7Y6VrcuvPUWPPXUI77zKDFRVRy9j+S0ZDou6cj+0P1MbDWRuT3m5usyH3ygqmmsWAFPPlmwrgohRHHKz+e37AITpVpwMLz8sipQ+uWXKvhp2jSjQOkzzzziwU90NLRsqRY73edvmcn/TmZ/6H4crByY0HJCvi7z9dfw4YdqsXlYWCH6K4QQpcSj/NEhSrHz52HGDLWkJS1NHWvXThUo7dHjIS1VkV/JyTBwoIoEb95USY4cHbNt+uvxX1l4aCEAv/X/jeoVq+f5MitXZlTPeP99dRkhhCjrJAASpcqxY2oH1x9/ZAxodO0K77yjNjRJ4HOXpsGLL4K/P9jYwD//5Bj8HIs4xtj1YwF4r8N79K6V9/LsW7eqUTZNUyNx779fJL0XQogSJwGQKBX271cbl9avzzj2xBNqxKdly5LrV6k1fbpKfGRqqoZomjTJttmtxFsMXDmQO6l36FmjJ+93ynsEc/gw9O8PKSkqqeT8+RKACiEeHhIAiRKjabB9O3z8sRrIAPUBO3QoTJv2ENfoKqxffskYilm4UGUkzMGR8CNcjbuKt4M3vw34DRNd3pf9/fQTxMdDly4qu7apaWE7LoQQpYcEQOKBSy9Q+sknkF6f1swMnn0Wpk6VvDK5Cg6G559XX7/xhpoGy0XXal3Z+9xeTHQmVLSumK9LLVigFp+/9JKqoyaEEA8T2QafDdkGXzzS0lSFhk8/zShObmkJY8aoz/KHvkZXUfnpJzVk9ssvYJL9iI6maegKMF8VH6+SSOZwWiGEKNVkG7woVVJS1Gd1gwYwZIgKfsqXhylTHqECpUXpuefUnFQOUcqlW5do8l0TDoYdzNdpb99WO+yefVZtMBNCiIdZvgMgLy8vPvroI65cuVIc/REPkTt3VOLCWrVg5Eg4dw4cHOC991SdrlmzwNW1pHtZBiQkqHmoa9cyjuUwunM75TYDVgzgROQJ/rflf+R1gDc1Va292rNHbSgLDi6CfgshRCmW7wBo0qRJrFmzhmrVqvH444+zfPlykpKSiqNvoozKXKD05ZfVh6mTk8rrc/mySqj3SBUoLYy0NHj6afjuO+jbN9dkh5qm8fI/L3M88jhO5ZxYOmBpnqbBNE3VT1u/Hqys4O+/ZR2WEOLhV6AA6NixYxw8eJC6desyYcIEXF1dGT9+PAEBAcXRR1FGxMSohc1eXjB5MoSHg7s7fPWVCoKmTgVZUpVPr78Of/6pFkt9+WWu+9C/Pfwtvxz/BROdCSsGrcDdzj1Pl5g61XhHffv2RdR3IYQozQpbeCw5OVmbO3euZmlpqZmYmGiNGzfWfvzxR02fQzHGskCKoebP9eua9vbbUqC0yM2dm/GCrliRa9N9Ifs084/MNT5A+3z353m+xBdfZFzip58K22EhhChZ+fn8LvA2+JSUFNauXcvixYvZsmULrVu3ZsyYMYSGhvLWW2+xdetWfv/996KL1ESpc/UqfPGFmp1JL1Bar54qUDp06CNeo6uw1q2D115TX3/2mVo9noNrCdcYtHIQKfoUBtYdyJS2U/J0idBQlWgy/RKjRxeyz0IIUYbk+yMqICCAxYsXs2zZMkxMTBgxYgRz5syhTp06hjb9+/enRYsWRdpRUXoEBakPzMWLM3YLNWumPkz79ZMt1IV28CAMH55R7uJ//8u1uaWpJT5uPtjesGXxk4vzvP3d3R02blQ76u9zCSGEeOjkOwBq0aIFjz/+ON988w39+vXD3Nw8Sxtvb2+eeuqpIumgKD3OnVMLmZcuzShQ+thjKvDp3l3KJBSZSpVUdFK9uspGeJ8X1t7KnrVD1xJ1OwpbS9v7nl6vzwhSO3dWNyGEeNTkOxHi5cuX8XzIk7ZIIkRj2RUoffzxjAKlohjcuAEWFmCbc0Bz8eZFqlWolq+Eh6dOqU1ly5dD3bpF0VEhhCg9ijUR4rVr1zhw4ECW4wcOHODw4cP5PZ0oxfbtgz59oGlTWLVKBT9PPqlmaDZvluCnSCUlwe7dGd9XqpRr8HP2+lmafNeEEetGkJiSmKdLXL6sRupOnFCZt4UQ4lGW7wBo3LhxhISEZDkeFhbGuHHjiqRTouRomloT4usLbduqpHgmJvDUU+qDc906kOVdRUzTVD2QTp3gxx/v2zwuKY4BKwcQnxxPaGwo5qZZp6Hvdf06dOumFq7Xrw8//1wE/RZCiDIs32uAzpw5Q7NmzbIcb9q0KWfOnCmSTokHT9NUsPPJJ7B/vzpmZgYjRqg8MTVrlmz/Hmrvv68WVpmZQdWquTbVNI3Rf47mXNQ5qthWYfnA5ZiZ5P7fOC4OevWC//5Tp9+0CSrmry6qEEI8dPI9AmRpaUlkZGSW4+Hh4ZjJvucyJy1NJb9r2lQlGt6/X+XcGzcOAgPVgIQEP8Vo8WKYPl19/e23anFVLmbvm83qs6sxNzHnjyF/4GzjnGv7pCQYMAAOHwZHRzV16Z63/IhCCPFQy3cA1K1bN6ZNm0ZMTIzhWHR0NG+99RaP3+eXtyg9UlLUNEj9+ipnz/HjYGOjtkMHB6vNRw/5WveSt3UrjB2rvn77bTUNlottQdt4c+ubAHzV4ytau7e+7yU++khdpnx52LABatcudK+FEOKhkO9dYGFhYXTo0IEbN27QtGlTAI4dO4azszNbtmzBw8OjWDr6ID3Mu8Du3FFlDz77LKPgpYMDTJwIr74qUyMPzKlT0K4dxMaqnD+//ZbrdvfElESqz6tOeHw4IxqPYMmTS/K0+ys6GgYPVoue5e8TIcTDLj+f3/kOgAASEhJYunQpx48fx9ramkaNGjFs2LBscwKVRQ9jAJSQoDI2f/GFqtEFULmyqtn18stSo+uBe/dd+PhjtZVu82Y173gf/kH+fLbnM9YNXYe1uXWeL6VpkqNJCPFoKPYA6GH3MAVA0dFqOmvuXJVaBtQakDfeUDMu5cqVZO8eYZoGixap4ZkiHnZLL02SXklDCCEeFfn5/C7wquUzZ85w5coVktNrIdz1xBNPFPSUoghdv66CngUL1CwLqMTCU6eqnV0WFiXavUdTaqr618xMDcm8+OJ9H7Lu3DrqOtaltmPeFu+sXq1G9DRNre/q1q0wHRZCiIdXvgOgS5cu0b9/f06ePIlOpyN9ACl9PUJaeo0EUSLCwtQ016JFxgVK335b1dOUjXolRNPUIqugILXtLpckh+mOhh9l2OphmJuYs//5/dRzqpdr+23bMkqIjR0ra36EECI3+d4FNnHiRLy9vbl27RrlypXj9OnT7Ny5k+bNm7N9+/Zi6KLIi6AgeOklqFZNjfzcvg0+PrBmDZw8qT4YJfgpQV9+Cd98A//+C3v23Lf5zcSbDFw5kDupd2jv2Z46jnVybR8QoLJ0Jyerbe9ffy3rfoQQIjf5/kjct28f/v7+ODo6YmJigomJCY899hgzZszg1Vdf5ejRo8XRT5GDs2dVgdLff88oUNq+vRrx6dZNPgRLhT/+gClT1NezZ0OPHrk212t6nlnzDEHRQVSrUI3f+v+GiS7nv1UCA6FnT5XwsFMnlVPR1LQI+y+EEA+hfI8ApaWlYXt3+N7R0ZGrV68C4Onpyfnz54u2dyJHR4+q9bP168Ovv6rgp1s32LEDdu6U6uylxr598Oyz6uvx42HSpPs+5KMdH7ExcCNWZlasGbKGCtYVcmwbH69+7teuqWSWf/4JVlZF1HchhHiI5XsEqEGDBhw/fhxvb29atWrF559/joWFBYsWLaJatWrF0UeRyd69qlzFhg0Zx/r1g7fekhpdpU5gIDzxhEq+1Levmpu8T1T6z3//8OGODwH4rs93NHZpnGt7GxuVw2nhQti4UdIZCCFEXuU7AHrnnXdISEgA4KOPPqJPnz60b9+eSpUqsWLFiiLvoMgoUPrJJ2qhK6gCpUOHwrRp0LBhyfZPZEPT1KrzqCi1GGvZsjzNS3135DsAXmn+CiMaj8jTpSZOVIuerfOeGkgIIR55RZIH6ObNm1SoUCFPmWnLgtKSByi9QOnHH8OBA+qYubnaxv7mm1Kjq9QLCFDTXmvWgItLnh6SkpbCgoMLGNdyHBam2ecqSE1V5cNee01l8RZCCKEUWyLElJQUrK2tOXbsGA0aNCh0R0urkg6A0tJUPpdPP1U1ukCt63j+eVWr6z4Fw0VpUsRpmDUNXnhBFalt2VItMTLJ90o+IYR4OOXn8ztfvzrNzc2pWrWq5PopJikpqk7XvQVK33hD1e2aP1+Cn1JvxgzYvz/j+zwEP18f+prXNr1GSlrKfdu+/bYKfkxM1PSnBD9CCFEw+f71+fbbb/PWW29x8+bNIuvEwoUL8fLywsrKilatWnHw4MFc269atYo6depgZWVFw4YN2ZB5RTAQGRnJqFGjcHNzo1y5cvTo0YMLFy4UWX+L2p07KkVMzZowejScPw8VKsAHH8Dly6pwqbNzSfdS3NeiRWo1uq8vhITk6SH7QvYxadMk5h6Yy8rTK3NtO3euiq9Albvo169w3RVCiEealk9NmjTRbGxsNEtLS61WrVpa06ZNjW75tXz5cs3CwkL76aeftNOnT2svvPCC5uDgoEVGRmbbfs+ePZqpqan2+eefa2fOnNHeeecdzdzcXDt58qSmaZqm1+u11q1ba+3bt9cOHjyonTt3Ths7dqxWtWpVLT4+Pk99iomJ0QAtJiYm388nP+LiNO2LLzTN1VXT1OSGplWurGmffaZpsbHFemlR1DZu1DRTU/VDfP/9PD0kIi5Cc5vtpvEB2uCVgzW9Xp9j299+y3iPfPppEfVZCCEeMvn5/M73IugPP/ww1/vff//9fAVgrVq1okWLFixYsAAAvV6Ph4cHEyZMYOrUqVnaDx06lISEBNavX2841rp1a5o0acK3337Lf//9R+3atTl16hT169c3nNPFxYVPP/2U559//r59Ku41QNHRajrrq6+yFih9/nnZzVPmHD8Ojz2mkvKMGKHmMe8z9ZWqT6XrL13ZcXkHdR3rcuD5A9haZl8e499/oU8ftfh54kSYM0dyPAkhRHaKtRhqfgOc3CQnJ3PkyBGmTZtmOGZiYkLXrl3Zt29fto/Zt28fkydPNjrWvXt31q1bB0BSUhIAVpmywZmYmGBpacnu3buzDYCSkpIMjwP1AhaH69fVh9fChRkFSmvUUAVKn31WCpSWSaGh0Lu3Cn46d4bvv89TdDJ161R2XN6BrYUta4auyTH4AbXuy9VVZfj+8ksJfoQQoiiU6BLKqKgo0tLScL5ngYuzszMRERHZPiYiIiLX9nXq1KFq1apMmzaNW7dukZyczGeffUZoaCjh4eHZnnPGjBnY29sbbh4eHkXw7LL6/nu1hiM2Vi10/v13VcpizBgJfsqk2FgV/ISFqYqza9bk6Qe58vRKZu+bDcCSfkvuW+erbl2VBmHxYln0LIQQRSXfv05NTEwwNTXN8VbSzM3NWbNmDf/99x8VK1akXLlybNu2jZ49e2KSw6fHtGnTiImJMdxC8riANb9eeUWtj127Fk6cgGHDpEBpmWZmBtWrqxXq//yT56Q8ZiZm2FjY8EbbNxhQd0C2ba5cgcy1hV1dJUgWQoiilO+P37Vr1xp9n5KSwtGjR/n555/vuz7oXo6OjpiamhIZGWl0PDIyEpccEse5uLjct72Pjw/Hjh0jJiaG5ORknJycaNWqFc2bN8/2nJaWllhaWuar7wXh4AB+fsV+GfGglCsHq1apHV9eXnl+2IC6A2js3BhPB89s74+KUrXcLl1SwXKvXkXUXyGEEAb5DoCefPLJLMcGDRpE/fr1WbFiBWPGjMnzuSwsLPDx8cHPz49+d/f06vV6/Pz8GD9+fLaPadOmDX5+fkzKVFRyy5YttGnTJktbe3t7AC5cuMDhw4eZPn16nvsmRI527VKLnnU6Vd4iD8GPpmlE3Y7CqbwTANUrVs+2XXy8mlU7d04tjJcyJ0IIUTyKbEVB69at8SvA8MbkyZP5/vvv+fnnnzl79iwvv/wyCQkJjB49GoARI0YYLZKeOHEimzZtYvbs2Zw7d44PPviAw4cPGwVMq1atYvv27Vy6dIk///yTxx9/nH79+tGtW7fCP1HxaFu+HDp0gJEjVcruPJq1dxYNv2nIjuAdObZJToZBg+DgQahYETZvhmJajiaEEI+8IlmBkpiYyLx586hSpUq+Hzt06FCuX7/Oe++9R0REBE2aNGHTpk2Ghc5XrlwxWrvTtm1bfv/9d9555x3eeustatasybp164xKc4SHhzN58mQiIyNxdXVlxIgRvPvuu4V/ouLRtnu3CnwAHB3zVNwUwD/In2l+09Bres5GnaWjV8csbfR6GDVKbXkvVw42bFCLn4UQQhSPfOcBurfoqaZpxMXFUa5cOX777TeeeOKJIu/kg1bStcBEKXT+PLRtCzdvQv/+au1PHgKgkJgQmi1qRtTtKEY1GcVPT/yUpWiwpsGkSTBvnlpX/fff0KNHMT0PIYR4iBVrHqA5c+YY/QI3MTExLDKuUKFC/nsrRGl3/bpaiXzzpqpA+ttveQp+klKTGLRqEFG3o2jq0pSve32dJfgBNfqTXlnm558l+BFCiAch3yNAjwIZARIGiYkqd8H+/eDtrcqv57Ew28vrX+bbI99SwaoCR8YewbuCd45t9Xo1w9ahQ1F1XAghHj3FVg0eYPHixaxatSrL8VWrVvHzzz/n93RClG7798PhwyqHwYYNeQ5+/jjzB98e+RYdOpYOWJpt8HP0aMY6ahMTCX6EEOJByncANGPGDBwdHbMcr1y5Mp9++mmRdEqIUqNzZ7Uyed06qJN7xubMetboyVMNnuKDTh/Qs2bPLPfv2AFt2qhkmJmqsAghhHhA8r0G6MqVK3h7Z/1r1tPTkytXrhRJp4QoccnJGamXfX3z/fDyFuX5fcDvaGSdYT52DJ54QgU+KSl53kwmhBCiCOV7BKhy5cqcOHEiy/Hjx49TqVKlIumUECXqn39UsbazZ/P1sDR9GstPLSd9WZ1Op8NEZ/xf7OJFtcg5NlZNef3+u5RDEUKIkpDvAGjYsGG8+uqrbNu2jbS0NNLS0vD392fixIk89dRTxdFHIR6cgAAYOhQCA+G77/L10A93fMiw1cN4Zu0z2d4fEaFKXERGQuPG8NdfYG1dFJ0WQgiRX/n+23P69OkEBwfTpUsXzO7+6arX6xkxYoSsARJl25Urqg5FQgI8/jjMmpXnh67/bz3Td6pSKz1rZF3zExMDPXuqESBvb9i4Ee5WahFCCFECCrwN/sKFCxw7dgxra2saNmyIp2f2hR3LItkG/wiKiYF27eD0aVWAa9euPEcogTcDab6oOTFJMYxvMZ75veZnabNzpxr9sbODPXugRo2ifgJCCCGKNRFiupo1a1KzZs2CPlyI0iM5GQYOVMGPq6taA5TH4Od2ym0GrBhATFIMbT3aMrv77GzbdeiganvZ2EjwI4QQpUG+1wANHDiQzz77LMvxzz//nMGDBxdJp4R4oKZPBz8/KF9eBT95rECqaRpj/x7LyWsncS7vzKrBq7Awtch0P1y7ltG+fXto2rSoOy+EEKIg8h0A7dy5k169emU53rNnT3bu3FkknRLigXrtNbXVfeXKfEUop6+fZuXplZjqTFk5eCVutm5G97/3HjRpAtlsmhRCCFHC8j0FFh8fj4WFRZbj5ubmxMbGFkmnhHigKlaErVshmzpduWlQuQE7R+/k9LXTdPA0TuM8fz58/LH6+tAhaNSoqDorhBCiKOR7BKhhw4asWLEiy/Hly5dTr169IumUEMVuxw745puM7/MZ/KRr7d6aMc3GGB1btgxefVV9PX06jBmTzQOFEEKUqHyPAL377rsMGDCAixcv4ns3Q66fnx+///47f/zxR5F3UIgid/Ys9OsH0dFqW9bTT+f5oSlpKTz/9/NMbj2Zxi6Ns9z/778wYoT6esIEePvtoumyEEKIopXvEaC+ffuybt06AgMDeeWVV3j99dcJCwvD39+fGrK9RZR2kZHQq5cKftq2hQED8vXwN7e+yS/Hf6H7b91JTEk0uu/AAbWZLDVV1fiaO7fAA0tCCCGKWYHzAKWLjY1l2bJl/Pjjjxw5coS09PLWZZjkAXpI3b4NnTqpRTk1asC+fZBNYd+crDi1gqdWq2zna4asoX/d/kb3d++utrp36wZ//51RSkwIIcSDkZ/P73yPAKXbuXMnI0eOxM3NjdmzZ+Pr68v+/fsLejohildamprqOnQIKlWCDRvyFfycvnaaMX+pxTxT203NEvwArFoFEyfC6tUS/AghRGmXrzVAERERLFmyhB9//JHY2FiGDBlCUlIS69atkwXQonSbMgXWrQNLS/jzT8hHEs+YOzEMWDmAhJQEulbryse+HxvuS0kBc3P1tZ2dmvYSQghR+uV5BKhv377Url2bEydOMHfuXK5evcr8+VlT/gtR6mgaVKkCJibw88+q5EWeH6ox6s9R/HfjPzzsPPh9wO+YmpgCqmRYx47w+efqEkIIIcqOPI8Abdy4kVdffZWXX35ZSmCIskWnUyNATzwBtWrl66G3U26TkJyAhakFq4esxqm8E6BGfgYPVsuIzp1TO79cXIqj80IIIYpDnkeAdu/eTVxcHD4+PrRq1YoFCxYQFRVVnH0TonBOnYLMyTnzGfwAlLcoz8anN7Jr9C5aVGkBgF4Pzz2nKrpbW8P69RL8CCFEWZPnAKh169Z8//33hIeH8+KLL7J8+XLc3NzQ6/Vs2bKFuLi44uynEPkTFARdusBjj8HVq/l+eEJyguFrUxNTWlZpCaiprilT4LffwNQU/vhD7aYXQghRtuR7F1j58uV57rnn2L17NydPnuT1119n5syZVK5cmSeeeKI4+ihE/ty6pXL9XLum1v3Y2ubr4XdS79Dp5068+PeLJKUmGd33+ecwZ476evFidRkhhBBlT4G3wQPUrl2bzz//nNDQUJYtW1ZUfRKi4JKSVHLDc+fUwud//sl3APTqxlc5fPUwf5z9g8iESMPxY8dg6lT19ezZ8OyzRdhvIYQQD1ShEyE+jCQRYhmlaWo18m+/qaBn9+58VyH9MeBHnv/7eXTo2PTMJrpV72Z0/9dfQ0gIzJhRlB0XQghRFPLz+Z3vWmBClFoffGC8OCefwc/hq4cZt2EcANM7T88S/AC88kpRdFQIIURJK9QUmBClRnQ0/Pij+vrbb1U9inyIuh3FwJUDSUpLom+tvkxrPw2AEyegZ0+4caOI+yuEEKJESQAkHg4ODrB/PyxcCM8/n6+HaprGs2uf5UrMFWpUrMEv/X/BRGdCUJCq77VpE7zxRvF0WwghRMmQAEiUbSkpGV+7uxdojkqn0zGh5QSq2FZhzZA1OFg5cO2aGkSKiICGDdWiZyGEEA8PCYBE2XX1KjRooNb7FFKvmr24+OpFGjo3JDZWTXsFBoKXlxoBcnAo9CWEEEKUIhIAibIpPh769oX//oN334Xk5Hyf4uLNiwTdCjJ8b2lmSVIS9O8PAQHg5ASbN4ObW1F2XAghRGkgAZAoe1JT4amnMqKUf/4BC4t8nSIhOYF+K/rhs8iH3Vd2G45PnAj+/mBjo0pdSNk7IYR4OJWKAGjhwoV4eXlhZWVFq1atOHjwYK7tV61aRZ06dbCysqJhw4Zs2LDB6P74+HjGjx+Pu7s71tbW1KtXj2+//bY4n4J4UDRNRSn//ANWVvDXX1CtWj5PofHC3y9w6topLM0sqV6huuG+11+H2rVh3Trw8SnivgshhCg1SjwAWrFiBZMnT+b9998nICCAxo0b0717d65du5Zt+7179zJs2DDGjBnD0aNH6devH/369ePUqVOGNpMnT2bTpk389ttvnD17lkmTJjF+/Hj++uuvB/W0RHGZM0dlI9TpVM6f1q3zfYr5B+ez7NQyzEzMWDV4Fa62rob7atZUNVS7dCnKTgshhChtSjwTdKtWrWjRogULFiwAQK/X4+HhwYQJE5iaXncgk6FDh5KQkMD69esNx1q3bk2TJk0MozwNGjRg6NChvPvuu4Y2Pj4+9OzZk48//vi+fZJM0KXUnj3Qvr0aBZo9GyZPzvcpdl/ZTeefO5OqT2Vu97lMbD2RRYugalXo0aMY+iyEEOKByc/nd4mOACUnJ3PkyBG6du1qOGZiYkLXrl3Zt29fto/Zt2+fUXuA7t27G7Vv27Ytf/31F2FhYWiaxrZt2/jvv//ols/keKKUadNGzVGNGwevvZbvh4fHhTN41WBS9akMazCMV1u9ysqV8NJLaj31iRPF0GchhBClUomWwoiKiiItLQ1nZ2ej487Ozpw7dy7bx0RERGTbPiIiwvD9/PnzGTt2LO7u7piZmWFiYsL3339Phw4dsj1nUlISSUkZVb9jY2ML+pREcTIxgVmz1AiQTpfvh0/fOZ2I+AgaVG7A932/x89PxzPPqNONHavy/QghhHg0lPgaoOIwf/589u/fz19//cWRI0eYPXs248aNY+vWrdm2nzFjBvb29oabh4fHA+6xyNGNG/Dmm6rKe7oCBD8AX3b/kldbvsqaIWs4e6I8/fqpPIpDhsC8eQU+rRBCiDKoREeAHB0dMTU1JTIy0uh4ZGQkLi4u2T7GxcUl1/aJiYm89dZbrF27lt69ewPQqFEjjh07xhdffJFl+gxg2rRpTM60niQ2NlaCoNLgzh3o109VdQ8LU4ueC8HKzIqven7Ff/+pRIcJCWqx8y+/qPqpomilpaWRkjlTtxBCFJK5uTmmRfQLu0QDIAsLC3x8fPDz86Nfv36AWgTt5+fH+PHjs31MmzZt8PPzY9KkSYZjW7ZsoU2bNgCkpKSQkpKCiYnx4JapqSl6vT7bc1paWmJpaVn4JySKjl4Po0er4MfODrJZEJ8XJyNPsvrsat7t8C6mJqZERqoSF1FRapv72rUgP/qipWkaERERREdHl3RXhBAPIQcHB1xcXNAVcti+RAMgUFvWR44cSfPmzWnZsiVz584lISGB0aNHAzBixAiqVKnCjBkzAJg4cSIdO3Zk9uzZ9O7dm+XLl3P48GEWLVoEgJ2dHR07duR///sf1tbWeHp6smPHDn755Re+/PLLEnueIp/efReWLwczM1izRpW8yKfoO9EMWDmAwJuB6DU9H3X+iEqVwNdXbSjbuBFsbYuh74+49OCncuXKlCtXrtC/pIQQAtQfV7dv3zakyXF1db3PI+5/whI3f/58rWrVqpqFhYXWsmVLbf/+/Yb7OnbsqI0cOdKo/cqVK7VatWppFhYWWv369bV//vnH6P7w8HBt1KhRmpubm2ZlZaXVrl1bmz17tqbX6/PUn5iYGA3QYmJiCv3cRAEsWqRpam2ypi1eXKBTpOnTtL6/99X4AK3qnKra9YTrhvv0ek2Liiqivgojqamp2pkzZ7QoeYGFEMUkKipKO3PmjJaamprlvvx8fpd4HqDSSPIAlaB//4XevSEtDd57Dz78sECn+XTXp7zt/zaWppZsf3YPR9b78OKLakBJFJ87d+4QFBSEl5cX1tbWJd0dIcRDKDExkeDgYLy9vbGysjK6r8zkARIiC0tLNS/17LPwwQcFOsXmi5t5x/8dABb0XMi37/swfjyMGFGE/RS5kmkvIURxKarfL/L3sChdOnWCQ4dUauYCvMmDo4MZtnoYGhrPN32e/1aM4eef1S6v4cOLvrtCCCHKJhkBEiUvLg4yJ76sUSPf1d3Tnbp2itspt2nu1pxq575m1ix1/McfoU+fIuirEPng5eXF3LlzS7obQohsSAAkSlZqqspE2Lo1bN9e6NP1qdWHfWP28VTqRt560xyAzz+HkSMLfWrxENPpdLnePijgdOyhQ4cYO3Zs0XZWCFEkZApMlBxNU3W9Nm0Ca2soX77Ap0rVp2Jmot7OoYeb8OYEdXzKFPjf/4qis+JhFh4ebvh6xYoVvPfee5w/f95wzMbGxvC1pmmkpaVhlocV9U5OTkXb0VIgP89fiNJMRoBEyfn8c1i0SK31WbYMWrQo0GkOhR2izoI6HAg9AKj1PhYWatHzZ58VZYfFw8rFxcVws7e3R6fTGb4/d+4ctra2bNy4ER8fHywtLdm9ezcXL17kySefxNnZGRsbG1q0aJGl3M69U2A6nY4ffviB/v37U65cOWrWrMlff/2Va99+/fVXmjdvjq2tLS4uLgwfPtyQByXd6dOn6dOnD3Z2dtja2tK+fXsuXrxouP+nn36ifv36WFpa4urqakg0GxwcjE6n49ixY4a20dHR6HQ6tt8dkd2+fTs6na5Azz8pKYk333wTDw8PLC0tqVGjBj/++COaplGjRg2++OILo/bHjh1Dp9MRGBiY62siRFGQAEiUjBUrMrI7z50LTz5ZoNNE3Y5i0KpBXLx1kdn7ZgOqzMXBg/DDD6p+qiglEhJyvt25k/e2iYl5a1vEpk6dysyZMzl79iyNGjUiPj6eXr164efnx9GjR+nRowd9+/blypUruZ7nww8/ZMiQIZw4cYJevXrx9NNPc/PmzRzbp6SkMH36dI4fP866desIDg5m1KhRhvvDwsLo0KEDlpaW+Pv7c+TIEZ577jlSU1MB+Oabbxg3bhxjx47l5MmT/PXXX9SoUeOBPP8RI0awbNky5s2bx9mzZ/nuu++wsbFBp9Px3HPPsXjxYqNrLF68mA4dOhSof0LkWxHnJ3ooSCLEYrZrl6ZZWqpEh5MmFfg0qWmpWtdfump8gOb1QUct4FRsEXZSFERiYqJ25swZLTExMeud6ckts7v16mXctly5nNt27Gjc1tEx+3YFtHjxYs3e3t7w/bZt2zRAW7du3X0fW79+fW3+/PmG7z09PbU5c+YYvge0d955x/B9fHy8BmgbN27Mc/8OHTqkAVpcXJymaZo2bdo0zdvbW0tOTs62vZubm/b2229ne19QUJAGaEePHjUcu3XrlgZo27Zt0zSt4M///PnzGqBt2bIl27ZhYWGaqampduDAAU3TNC05OVlzdHTUlixZct/riEdbbr9n8vP5LX8fiwfvq69Udfd+/eCeIfD8eHfbu2y9tBXr5Krw67/08LXl+PGi66YQmTVv3tzo+/j4eKZMmULdunVxcHDAxsaGs2fP3ncEqFGjRoavy5cvj52dXZYprcyOHDlC3759qVq1Kra2tnTs2BHAcJ1jx47Rvn17zM3Nszz22rVrXL16lS5duuT5eeYkv8//2LFjmJqaGvp7Lzc3N3r37s1PP/0EwN9//01SUhKDBw8udF+FyAtZxSYevN9+g4YN1QrlAlb1XXduHTN2z4AkG1z/CuDSRUuqVgVHxyLuqyg68fE533fv+yCXgCDLvGZwcIG7lB/l71mkP2XKFLZs2cIXX3xBjRo1sLa2ZtCgQSQnJ+d6nnsDFZ1Ol2Oh5oSEBLp370737t1ZunQpTk5OXLlyhe7duxuuk1vG7ftl404vGq1lKgiQkpKSbdv8Pv+8ZAJ//vnnefbZZ5kzZw6LFy9m6NChlCtX7r6PE6IoSAAkHozU1Iw6FJaWqsxFAV24cYGR60ZCqgVVNx3m0ulKODrC5s1QpUoR9VcUvfzs8iuutkVoz549jBo1iv79+wNqRCS4iIOxc+fOcePGDWbOnImHhwcAhw8fNmrTqFEjfv75Z1JSUrIEV7a2tnh5eeHn50fnzp2znD99l1p4eDhNmzYFMFoQnZv7Pf+GDRui1+vZsWMHXbt2zfYcvXr1onz58nzzzTds2rSJnTt35unaQhQFmQITxU+vh2HDYPJkVeOrkCqXr0ynqr44bt7AlaO1KV8eNmyA2rWLoK9C5FHNmjVZs2YNx44d4/jx4wwfPjzHkZyCqlq1KhYWFsyfP59Lly7x119/MX36dKM248ePJzY2lqeeeorDhw9z4cIFfv31V8M2/g8++IDZs2czb948Lly4QEBAAPPnzwfUKE3r1q0Ni5t37NjBO++8UyTP38vLi5EjR/Lcc8+xbt06goKC2L59OytXrjS0MTU1ZdSoUUybNo2aNWvSpk2bwr5kQuSZBECi+E2dCn/8AQsWwKlThT6dnaU9HnvXEHWwC+bmsHZtgXfQC1FgX375JRUqVKBt27b07duX7t2706xZsyK9hpOTE0uWLGHVqlXUq1ePmTNnZtk6XqlSJfz9/YmPj6djx474+Pjw/fffG0aDRo4cydy5c/n666+pX78+ffr04cKFC4bH//TTT6SmpuLj48OkSZP4+OOP89S3vDz/b775hkGDBvHKK69Qp04dXnjhBRLu2aE3ZswYkpOTGT16dEFeIiEKTKrBZ0OqwRehb76BV15RX//2Gzz9dIFPdTziOI2cG3H7to4uXdRW92XLYOjQIuqrKLT0avDZVWkWIju7du2iS5cuhISE4OzsXNLdEWVAbr9npBq8KB02bIC7CdeYPr1Qwc/OyzvxWeTDM2ufwdwqGT8/NfIjwY8QZVNSUhKhoaF88MEHDB48WIIf8cBJACSKR0CAqvGl18Po0fD22wU+1dW4qwxZNYS0m+7o0GFuYk758gXOnSiEKAWWLVuGp6cn0dHRfP755yXdHfEIkgBIFL3bt+GJJ1Q23q5d4bvvVLmLAkhOS2bwqsFEnqyHbuF/eJ/4CSjYuYQQpceoUaNIS0vjyJEjVJHtm6IESAAkil65cirZYYsWavFzNgna8mrK5insPZAIK9ahpVpw9pQFsmpNCCFEYUkAJIrHwIGwfz/Y2xf4FEtPLGX+xo3w2yZIssPXF5YulfpeQgghCk8+SkTR0DS10DkkJONYISKV6DvRvLT8Q/h1M9yuTLNmatGzpWUR9FUIIcQjTwIgUTQ+/VRld27XTq0BKqw7DjivDYBob2rU0NiwASQjgRBCiKIiAZAovKVLIT177FtvqTVAhbR+PVw8Z4OLC2zerEN2yAohhChKUgtMFM6OHfDcc+rr//0PXnqpUKf7/sj3tPdszzPP1CElBZo1A2/vIuinEEIIkYmMAImCO3sW+vWD5GQYNAhmzizU6TZd+JexaybR4vsWXI6+zOjR0Lhx0XRViJLg5eXF3LlzS7obD5Xk5GRq1KjB3r17i+X8U6dOZcKECcVyblG6SAAkCiYyEnr1guhoaNMGfvmlUIueg6OD6T/2LCzZTn+PsXg6eBZdX4W4D51Ol+vtgw8+KNB5Dx06xNixY4u2s4+4b7/9Fm9vb9q2bWs4lvlnVb58eWrWrMmoUaM4cuSI0WO3b9+e4884IiICgClTpvDzzz9z6dKlB/q8xIMnAZAomLQ0tcW9enX480+wti7wqRJTEnls7GrubJ8EV1vQzaRwI0lC5Fd4eLjhNnfuXOzs7IyOTZkyxdBW0zRSU1PzdF4nJyfKFcGauNIkP8+/OK69YMECxowZk+W+xYsXEx4ezunTp1m4cCHx8fG0atWKX375JUvb8+fPG/18w8PDqVy5MgCOjo50796db775ptifjyhZEgCJgnFzg127YMsWcHIq8Gk0TaPHG78Stup1AN58P5pnhhc8caIQBeHi4mK42dvbo9PpDN+fO3cOW1tbNm7ciI+PD5aWluzevZuLFy/y5JNP4uzsjI2NDS1atGDr1q1G5713Ckyn0/HDDz/Qv39/ypUrR82aNfnrr79y7duvv/5K8+bNsbW1xcXFheHDh3Pt2jWjNqdPn6ZPnz7Y2dlha2tL+/btuXjxouH+n376ifr162NpaYmrqyvj79boCw4ORqfTcezYMUPb6OhodDod27dvBzJGTQry/JOSknjzzTfx8PDA0tKSGjVq8OOPP6JpGjVq1MhS2f7YsWPodDoCAwOzfS2OHDnCxYsX6d27d5b7HBwccHFxwcvLi27duvHHH3/w9NNPM378eG7dumXUtnLlykY/cxcXF0wyjWD37duX5cuX5/ATEQ8LCYBE/mT6RYmtbaFXKE+cv4md80YDMPC5K8x436FQ5xOlV0JyQo63O6l38tw2MSUxT22L2tSpU5k5cyZnz56lUaNGxMfH06tXL/z8/Dh69Cg9evSgb9++XLlyJdfz/L+9+w6L4ujjAP49yh0dVLoioGBQpNgFg9iSsxFAUSwRCIqJ7UWNiSEWUGOLGmtC1ETQxJ4I0aAYRDAES9RIU0QlIErADnqItJv3jwsbjjuqyKH8Ps+zz3O7Mzs7u3vcDrNTli1bhvHjxyMlJQUjR47E5MmT8fjx4xrjl5WVYcWKFUhOTkZkZCSys7Ph5+fHhefm5mLgwIEQCAQ4ffo0Ll++DH9/f66WJjQ0FLNmzcL06dORmpqKo0ePwsrKqlnO38fHB/v378eWLVuQnp6O7du3Q0tLCzweD/7+/ggLC5M6RlhYGAYOHFhj/hISEtClSxdoa2vXK8/z5s3Ds2fPEBMT06Bz7du3L+7evYvs7OwG7UdeM4zIKCwsZABYYWGhorPSsmzZwhjA2Jo1TZJcYqKYKfGLGcBYj3dTWUVFkyRLFKi4uJhdu3aNFRcXy4QhBDUuI/eOlIqrsVKjxriuYa5ScfW/1Jcbr7HCwsKYrq4utx4XF8cAsMjIyDr3tbW1ZVu3buXWzc3N2caNG7l1AGzx4sXcukgkYgDYiRMn6p2/ixcvMgDs2bNnjDHGgoKCmKWlJSstLZUb39TUlC1atEhuWFZWFgPArly5wm178uQJA8Di4uIYY40//4yMDAaAxcTEyI2bm5vLlJWV2YULFxhjjJWWljJ9fX0WHh5eY/qBgYFsyJAhMtsBsIiICJntxcXFDABbu3at1LloampKLd26dZPar/IZEB8fX+c5k+ZX2+9MQ57f1A2e1M/Ro8DcuZLPTTAZl1gMBATwIC5Vg92AOzh/zJamuCAtWu/evaXWRSIRQkJCEBUVhby8PJSXl6O4uLjOGiB7e3vus6amJnR0dGReaVV1+fJlhISEIDk5GU+ePIFYLAYA5OTkoFu3bkhKSoKLiwtU5cy5d//+ffzzzz8YOnRoQ05Vroaef1JSEpSVleHq6io3PVNTU4waNQq7du1C3759cezYMZSUlGDcuHE15qG4uBhqamr1zjP797eKV20y5oSEBKlapOrXTv3fNo3Pm2JQV9JiUQGI1O3SJWDiREmpZfp0YOHCl0qOMQYlJR6OHQOWLAF27DADn99EeSUtlihIVGOYspKy1Pr9BTUXCJR40iXl7MDsl8pXfWlqakqtL1iwADExMVi/fj2srKygrq4OLy8vlJaW1ppO9Yctj8fjCjXVFRUVQSgUQigUYu/evTAwMEBOTg6EQiF3HPVaOiDUFgaAa/fCqvxTU1ZWJjduQ8+/rmMDwLRp0zBlyhRs3LgRYWFh8Pb2rrXRuL6+PlJTU+tMt1J6ejoAwLLaq3pLS0vo6enVuF/lK0mDl2jfSFo+KgCR2mVnA6NHS6a3GD4c+PproNp/Uw3BGBAUG4SS8hJ8+c6X2LuXGjy3Fpp8zbojveK4TSkxMRF+fn7w9PQEIKkRaeo2I9evX8ejR4+wZs0amJmZAQAuXbokFcfe3h67d+9GWVmZTOFKW1sbFhYWiI2NxeDBg2XSr3zA5+XloUePHgAg1SC6NnWdv52dHcRiMc6cOYNhw4bJTWPkyJHQ1NREaGgooqOj8fvvv9d6zB49eiA0NBSMMZlaHXkqe/TVdPyapKWlQVVVFba2tg3aj7xe6KUDqVlBgWSsn3v3JCMSHjoEqDS+zCwSAXb9H2DtrjRsurAJcdlxTZdXQpqZtbU1jhw5gqSkJCQnJ2PSpEk11uQ0VseOHcHn87F161b8/fffOHr0KFasWCEVZ/bs2Xj69CkmTJiAS5cu4ebNm/jhhx+QkZEBAAgJCcGGDRuwZcsW3Lx5E3/99Re2bt0KQFJL079/f65x85kzZ7C4clqblzx/CwsL+Pr6wt/fH5GRkcjKykJ8fDwOHTrExVFWVoafnx+CgoJgbW0NJyenWo85ePBgiEQiXL16VSasoKAA+fn5uH37NmJiYuDl5YV9+/YhNDRUprbn/v37yM/Pl1qq1nwlJCTAxcWlXrVY5PVFBSBSs59/loz23L49EBUl6fXVSKWlwIj3inD1TwPg6HeY4/gZ3u38bhNmlpDm9dVXX6FNmzZwdnaGm5sbhEIhevbs2aTHMDAwQHh4OA4fPoxu3bphzZo1Ml3H27Vrh9OnT0MkEsHV1RW9evXCzp07udogX19fbNq0Cd988w1sbW0xevRo3Lx5k9t/165dKC8vR69evTB37lx88cUX9cpbfc4/NDQUXl5emDlzJmxsbBAQEICiIukeelOnTkVpaSk++OCDOo/Zrl07eHp6Yu/evTJhH3zwAUxMTGBjY4MZM2ZAS0sLf/75JyZNmiQT96233oKJiYnUUnXQxAMHDiAgIKBe14G8xpq6dXZjbNu2jZmbmzOBQMD69u3L9QqoyaFDh9hbb73FBAIB6969O4uKipIKByB3+fLLL+uVH+oFVsWOHYwlJb1UEhUVjI3zLmUAY1AVMcfFH7LScvk9VsjrrbbeGYTI8/vvvzNVVVWWn59fr/jJycnM0NCQ6wXX1I4fP866du3KysrKXkn65OU1VS8whdcAHTx4EPPnz0dwcDD++usvODg4QCgU1tgr4uzZs5g4cSKmTp2KK1euwMPDAx4eHkhLS+PiVB/hc9euXeDxeBg7dmxzndbrraLiv88BAS81IRdjwNy5DIcPqgJKZWjrF4ATC0OgqkxtfwhpzUpKSnD37l2EhIRg3LhxMDIyqtd+9vb2WLt2LbKysl5JvoqKihAWFgaVl3jdT14Tr6J01hB9+/Zls2bN4tYrKiqYqakpW716tdz448ePZ6NGjZLa1q9fP/bhhx/WeAx3d3e5Y0fUpFXXAIWHM+biwtjDh02S3MqVkqGDAMaUvN5niTmJTZIuaZmoBojUV1hYGFNSUmI9e/Zkd+/eVXR2yGvkjagBKi0txeXLl6Va6CspKWHYsGE4d+6c3H3OnTsn06JfKBTWGP/evXuIioqSO3dMpZKSEjx9+lRqaZViY4Fp0yRTXFQbobUxfvsNWLRI8pk/6lNs/qQfnM2ca9+JENIq+Pn5oaKiApcvX0b79u0VnR3SCim0ju/hw4eoqKiQqfo0MjLC9evX5e6Tn58vN37lTL7V7d69G9ra2hgzZkyN+Vi9ejWWLVvWwNy/Ya5eBcaOBcrLgQkTgPnzXzrJoUOBDz8E2rUDZi4MhKm2aRNklBBCCHl5b/xLzl27dmHy5Mm1jh4aFBSE+VUe+E+fPuXG3GgV8vMl3d0LC4G335bU/rzksMylFaV4+PwhQkMlhR4ej/7DI4QQ0nIotACkr68PZWVl3Lt3T2r7vXv3YGxsLHcfY2PjesdPSEhARkYGDh48WGs+BAIBBAJBA3P/higqkgx0mJMDWFsDkZFAA4aar+7KFeC77wAm/AQ/ZezHoXGHMMhiUJNllxBCCGkKCm0DxOfz0atXL8TGxnLbxGIxYmNjaxwQy8nJSSo+AMTExMiN//3336NXr15weIleTG+8GTOAy5cBfX3gxAnJ+6pGyswERowAvvkGCN3QDg+eP4CotObpDwghhBBFUXg3+Pnz52Pnzp3YvXs30tPTMWPGDBQVFXGDYvn4+CAoKIiLHxgYiOjoaGzYsAHXr19HSEgILl26hNmzZ0ul+/TpUxw+fBjTpk1r1vN57SxaBNjaSiY77dy50cnk5wPvvisZNJpnnAI4b8DSgUsxusvoJswsIYQQ0jQU3gbI29sbDx48wNKlS5Gfnw9HR0dER0dzDZ1zcnK4CfsAwNnZGfv27cPixYvx+eefw9raGpGRkejevbtUugcOHABjDBMnTmzW83ntvPUWkJwMKCvXHbcGhYWSacL+/htQaXcb5ZPfxfDuzljqurQJM0oIIYQ0HR5jVaYBJgAktUe6urooLCyEjo6OorPT9CIjAS0toIETBMrz4gUgFAK//w7wdZ+g1Lc3LDqJcXn6ZbRVb/vyeSWvlRcvXiArKwuWlpa1djxoLSwsLDB37lzMnTtX0Vl5Y5SWlqJbt27Ys2cPnJ1pWA0/Pz8UFBQgMjKy0Wl89tlnKCoq4uaIa+lq+51pyPNb4a/ASDO7cAGYOFHSWOfs2ZdOzs9PUvhR0yxB6YQhUDP8B0fGH6HCD3mt8Hi8WpeQkJBGpXvx4kVMnz69aTPbyn377bewtLSUKvxUvVeampqwtraGn5+f1PxeABAfH1/jPa5pKBVFsLCwwKZNm+oVd/PmzQgPD69X3OzsbPB4PCQlJUltX7BgAXbv3o2///67YRl9zVEBqDX5+2/Aze2/apu+fV86yalTJe2mj0SIMendbvh21LfoYdKjCTJLSPOpOnXOpk2boKOjI7VtwYIFXFzGGMrLy+uVroGBATQ0NF5VthWiIef/Ko69bds2uQPbhoWFIS8vD1evXsXXX38NkUiEfv36Yc+ePTJxMzIyZKZMMjQ0lHtMPz+/BhWAq84q/ypVVFRALBZDV1dXZrb7htLX14dQKERoaGjTZO41QQWg1uLxY8lYPw8eAD17AgcOAE0w18077wDZ2cCId9Txo+eP8HX0ffm8EtLMjI2NuUVXVxc8Ho9bv379OrS1tXHixAn06tULAoEAf/zxBzIzM+Hu7g4jIyNoaWmhT58+OHXqlFS61f+T5/F4+O677+Dp6QkNDQ1YW1vj6NGjtebthx9+QO/evaGtrQ1jY2NMmjRJZq7Eq1evYvTo0dDR0YG2tjZcXFyQmZnJhe/atQu2trYQCAQwMTHhOo3IqxEoKCgAj8dDfHw8gP9qTRpz/iUlJVi4cCHMzMwgEAhgZWWF77//HowxWFlZycxsn5SUBB6Ph1u3bsm9FpcvX0ZmZiZGjRolE6anpwdjY2NYWFjg3XffxU8//YTJkydj9uzZePLkiVRcQ0NDqXtubGws1da0IXg8HkJDQ/Hee+9BU1MTK1euBAD88ssv6NmzJ9TU1NCpUycsW7aMKzgyxhASEoKOHTtCIBDA1NQU//vf/wAAgwYNwu3btzFv3jyudgoAwsPDoaenh6NHj6Jbt24QCATIycmBn58fPDw8uPyIxWJ8+eWXsLKygkAgQMeOHbk8WVpaAgB69OgBHo+HQYMGcfu5ubnhwIEDjboGrysqALUGJSWAhweQkQGYmQG//ippA9RIYWHA9etAcVkxdlzeAQ1NMQBwf6iEyFNUVPPy4kX94xYX1y9uU/vss8+wZs0apKenw97eHiKRCCNHjkRsbCyuXLmC4cOHw83NDTk5ObWms2zZMowfPx4pKSkYOXIkJk+ejMePH9cYv6ysDCtWrEBycjIiIyORnZ0NPz8/Ljw3NxcDBw6EQCDA6dOncfnyZfj7+3MP29DQUMyaNQvTp09Hamoqjh49Cisrq2Y5fx8fH+zfvx9btmxBeno6tm/fDi0tLfB4PPj7+yOs2pQ7YWFhGDhwYI35S0hIQJcuXaCtrV2vPM+bNw/Pnj1DTExMg8+3IUJCQuDp6YnU1FT4+/sjISEBPj4+CAwMxLVr17B9+3aEh4dzBZGff/4ZGzduxPbt23Hz5k1ERkbCzs4OAHDkyBF06NABy5cv52qnKj1//hxr167Fd999h6tXr8qttQoKCsKaNWuwZMkSXLt2Dfv27eM6Ff35558AgFOnTiEvLw9Hjhzh9uvbty/u3r2L7OzsV3WZWp6mnKDsTfFGTYZaUcHYxImS2Uh1dBhLTX2p5PbulSTVrp2YeX0XyBACNvWXqU2UWfK6q22SwspJceUtI0dKx9XQqDmuq6t0XH19+fEaKywsjOnq6nLrcXFxDACLjIysc19bW1u2detWbt3c3Jxt3LiRWwfAFi9ezK2LRCIGgJ04caLe+bt48SIDwJ49e8YYYywoKIhZWlqy0tJSufFNTU3ZokWL5IZlZWUxAOzKlSvctidPnjAALC4ujjHW+PPPyMhgAFhMTIzcuLm5uUxZWZlduHCBMcZYaWkp09fXZ+Hh4TWmHxgYKHdiawAsIiJCZntxcTEDwNauXSt1LpqamlJLt27dajymr68vCw4OrjEcAJs7d67UtqFDh7JVq1ZJbfvhhx+YiYkJY4yxDRs2sC5dutR4z6p/bxiTfC8BsKSkJJn8ubu7M8YYe/r0KRMIBGznzp1y05V3vytVPvfi4+NrOtUW442YDJU0g4oKgM+XvO76+Weg2nABDREdDfj++4bLflgafrqzGUo8JUyym9REmSWk5erdu7fUukgkwoIFC9C1a1fo6elBS0sL6enpddYA2dvbc581NTWho6Mj80qrqsuXL8PNzQ0dO3aEtrY2XF1dAYA7TlJSElxcXKCqqiqz7/379/HPP/9g6NCh9T7PmjT0/JOSkqCsrMzltzpTU1OMGjUKu3btAgAcO3YMJSUlGDduXI15KC4ublDvQvZvJ+fqtdMJCQlISkriluPHj3Nhe/fuhZaWFrfs3bsXq1atktqWkJAglV71a5OcnIzly5dL7RMQEIC8vDw8f/4c48aNQ3FxMTp16oSAgABERETUq10Vn8+X+v5Ul56ejpKSkkbdb3V1dQCSWqbWQuHjAJFXTFVV8s4qMBDo0fjGyRcu/DdX6rvuD3G6q+QPfs3QNRhiOaSpckveYKJaBgWvPgxVLeUBmWnqmqvGXlNTU2p9wYIFiImJwfr162FlZQV1dXV4eXmhtLS01nSqF1R4PB7EYrHcuEVFRRAKhRAKhdi7dy8MDAyQk5MDoVDIHafywSVPbWEAuHYvlQUFoOZGvA09/7qODQDTpk3DlClTsHHjRoSFhcHb27vWRuP6+vpITU2tM91K6enpAP5r+1LJ0tKyxobD7733Hvr168etL1y4EO3bt+fa6ACQmb2++rURiURYtmyZ3Em41dTUYGZmhoyMDJw6dQoxMTGYOXMm1q1bhzNnzsgtyFZSV1evtalBfa55TSpfwxoYGDQ6jdcNFYDeVNeuSQY5VFYGeLyXKvykpwOjRgHPnwODhpYgzbkPyotLMabrGCxwXlB3AoQAqPaMUEjcppSYmAg/Pz94enoCkDz0mrr9xPXr1/Ho0SOsWbOGm6D50qVLUnHs7e2xe/dulJWVyTw8tbW1YWFhgdjYWAwePFgm/cqHXV5eHnr8+xtRvYt0Teo6fzs7O4jFYpw5cwbDahhzbOTIkdDU1ERoaCiio6Px+++/13rMHj16IDQ0FIyxerU5rOzRV9Px5dHW1pZqY6StrY22bds2qN1Uz549kZGRUes+6urqcHNzg5ubG2bNmgUbGxukpqaiZ8+e4PP5qKioqPfxKllbW0NdXR2xsbFyZ0Hg8/kAIDfttLQ0qKqqwtbWtsHHfV1RAehNlJIimdV98GBg/37gJbrh3r0r6TH/6BHQpy9DxThP/JOfDRt9G4S5h1HDZ9JqWVtb48iRI3BzcwOPx8OSJUtqrMlprI4dO4LP52Pr1q346KOPkJaWhhUrVkjFmT17NrZu3YoJEyYgKCgIurq6OH/+PPr27Yu33noLISEh+Oijj2BoaIgRI0bg2bNnSExMxJw5c6Curo7+/ftjzZo1sLS0xP3797F48eImOX8LCwv4+vrC398fW7ZsgYODA27fvo379+9j/PjxAABlZWX4+fkhKCgI1tbWNc4BWWnw4MEQiUS4evWqzOj/BQUFyM/PR0lJCW7cuIHt27cjMjISe/bskantuX//Pl5Ua3nfrl27WmtfGmLp0qUYPXo0OnbsCC8vLygpKSE5ORlpaWn44osvEB4ejoqKCvTr1w8aGhr48ccfoa6uDnNzcwCSa/f7779jwoQJEAgE0NfXr9dx1dTUsHDhQnz66afg8/kYMGAAHjx4gKtXr2Lq1KkwNDSEuro6oqOj0aFDB6ipqUFXVxeA5LWgi4vLS9UivW6oDdCbJjdXUl3z7JlkjoqXmOICkPx33bGjpDJp+c7LOH//FLT4Wjgy/gh0BG/gKNmE1NNXX32FNm3awNnZGW5ubhAKhejZs2eTHsPAwADh4eE4fPgwunXrhjVr1sh0HW/Xrh1Onz4NkUgEV1dX9OrVCzt37uQe5r6+vti0aRO++eYb2NraYvTo0bh58ya3/65du1BeXo5evXph7ty5+OKLL+qVt/qcf2hoKLy8vDBz5kzY2NggICAARdW66E2dOhWlpaXc/I+1adeuHTw9PbF3716ZsA8++AAmJiawsbHBjBkzoKWlhT///BOTJsm2UXzrrbdgYmIitVQfNPFlCIVC/Prrr/jtt9/Qp08f9O/fHxs3buQKOHp6eti5cycGDBgAe3t7nDp1CseOHUO7fyejXr58ObKzs9G5c+cGv5JasmQJPv74YyxduhRdu3aFt7c318ZMRUUFW7Zswfbt22Fqagp3d3duvwMHDiAgIKCJrsDrgabCkOO1nQrj2TPAxUUyt5eNjWSk5zZtXjrZ58+BggLA1BS4cPcC7hXdw3tvvffy+SVvHJoKgzRUQkIChg4dijt37nDdtWuTkpKCd955B5mZmdB6ieE8yH9OnDiBjz/+GCkpKVBpgvHhXjWaCoNIKy8Hxo+XFH4MDYHjxxtd+Ckrk0wXVklDQ1L4AYB+HfpR4YcQ8tJKSkpw9+5dhISEYNy4cfUq/ACSNk9r165FVlbWK85h61FUVISwsLDXovDTlKgA9CZgDJg1S9JPXV0dOHYMqNbrob7EYsDfH/D0BFasAJ6VPMPofaORlJ/UtHkmhLRq+/fvh7m5OQoKCvDll182aF8/Pz9u4EDy8ry8vKR6vrUWVAB6E9y8Cfzwg6S31/79jZ7jizFgwQLgxx8lTYd69mTwP+qPqJtRGH94PMrFipn/hxDy5vHz80NFRQUuX74s062ckObQuuq73lRdugDx8ZLXX1UatTXUl18CGzdKPoeFAeltNuCnSz9BVUkVezz3QEWJvi6EEELeDPREe52Jxf+NCte370vN7v7998Bnn0k+b9gAdHg7Dn4/LAQAbB6+Gf079H/Z3BJCCCEtBr0Ce13dvAk4OABN0HXzl1+A6dMlnxcuBMZPuwvvn7whZmL4OPjgo94fvfQxCCGEkJaECkCvo4cPgZEjgbQ0SaOdlxzJIDtbUpn0wQdAyIoSeB3ywoPnD+Bg5IDQUaE02CEhhJA3Dr0Ce928eCFp53PrFmBhARw4IGn8/BICAwFbW2DQIKCo/AV01XShp6aHI95HoKHa+FGkCSGEkJaKCkCvE7EY8PGRDHCopycZ66eeY2dUl5MD6OpKFgConCpHV0UXxycdx41HN9CpTaemyTchhBDSwtArsNdJUBBw+LBkhvcjR4CuXRuVzL17wNChkhqf/HzJtsfFj7lwZSVldDVoXNqEtGaDBg3C3LlzuXULCwts2rSp1n14PB4iq4482khNlQ4hrQUVgF4XBw9K+qkDki5bcmZ2ro+nT4ERIyRv0AoKJM2HHhc/Ru8dvRFwNAAvyl/UmQYhbxo3NzcMHz5cblhCQgJ4PB5SUlIanO7FixcxvbKHQRMJCQmBo6OjzPa8vDyMGDGiSY9FyJuMXoG9LkaNkix9+wJTpjQqiRcvAA8P4MoVwMAA+O03wMhYjNH73kdWQRZis2JRXFYMNRWaw4m0LlOnTsXYsWNx9+5ddOjQQSosLCwMvXv3hr29fYPTbehEli/D2Ni42Y7VkpSWloLP5ys6G+Q1RDVArwstLUl/9SVLGrV7RQXw/vtAXBygrS2ZNcPaGlh+ZjlO3DoBNRU1HPE+gjbqLz95KiFSGAOKihSz1LOH5OjRo7mZ16sSiUQ4fPgwpk6dikePHmHixIlo3749NDQ0YGdnh/3799eabvVXYDdv3sTAgQOhpqaGbt26ISYmRmafhQsXokuXLtDQ0ECnTp2wZMkSlJWVAQDCw8OxbNkyJCcng8fjgcfjcXmu/gosNTUVQ4YMgbq6Otq1a4fp06dDJBJx4X5+fvDw8MD69ethYmKCdu3aYdasWdyx5MnMzIS7uzuMjIygpaWFPn364NSpU1JxSkpKsHDhQpiZmUEgEMDKygrff/89F3716lWMHj0aOjo60NbWhouLCzIzMwHIvkIEAA8PD/j5+Uld0xUrVsDHxwc6OjpcDVtt163SsWPH0KdPH6ipqUFfXx+enp4AJLOvd+/eXeZ8HR0dsaSRv7mk5aMaoJbszh3g558l3bR4PMn8FI1QOVXYzz8DfL5kotOePYGoG1FYdmYZAGD76O1wNHZsurwTUun5c0kBXhFEIkBTs85oKioq8PHxQXh4OBYtWsQN/XD48GFUVFRg4sSJEIlE6NWrFxYuXAgdHR1ERUVhypQp6Ny5M/rWYxBSsViMMWPGwMjICBcuXEBhYaHMwx4AtLW1ER4eDlNTU6SmpiIgIADa2tr49NNP4e3tjbS0NERHR3MFD93KngxVFBUVQSgUwsnJCRcvXsT9+/cxbdo0zJ49W6qQFxcXBxMTE8TFxeHWrVvw9vaGo6MjAgICaricIowcORIrV66EQCDAnj174ObmhoyMDHTs2BEA4OPjg3PnzmHLli1wcHBAVlYWHj58CADIzc3FwIEDMWjQIJw+fRo6OjpITExEeXnDptlZv349li5diuDg4HpdNwCIioqCp6cnFi1ahD179qC0tBTHjx8HAPj7+2PZsmW4ePEi+vTpAwC4cuUKUlJScOTIkQbljbxGGJFRWFjIALDCwkLFZaKggLHu3RkDGFu+/KWSystjrH17xng8xg4flmzLfJzJ9NboMYSAzfx1ZhNkmBDGiouL2bVr11hxcfF/G0UiyfdYEYtIVO+8p6enMwAsLi6O2+bi4sLef//9GvcZNWoU+/jjj7l1V1dXFhgYyK2bm5uzjRs3MsYYO3nyJFNRUWG5ublc+IkTJxgAFhERUeMx1q1bx3r16sWtBwcHMwcHB5l4VdPZsWMHa9OmDRNVOf+oqCimpKTE8vPzGWOM+fr6MnNzc1ZeXs7FGTduHPP29q4xL/LY2tqyrVu3MsYYy8jIYABYTEyM3LhBQUHM0tKSlZaWyg2vfv0YY8zd3Z35+vpy6+bm5szDw6POfFW/bk5OTmzy5Mk1xh8xYgSbMWMGtz5nzhw2aNCgOo9Dmp/c35l/NeT5TTVALVFZGTBunGSgQ2NjwNf3pZIzNgYSEyWLlxcgZmKMOzwOBS8K0L9Df2wcvrGJMk6IHBoakpoYRR27nmxsbODs7Ixdu3Zh0KBBuHXrFhISErB8+XIAQEVFBVatWoVDhw4hNzcXpaWlKCkpgUY9j5Geng4zMzOYmppy25ycnGTiHTx4EFu2bEFmZiZEIhHKy8uho6NT7/OoPJaDgwM0q9R+DRgwAGKxGBkZGTD6d/gMW1tbKFepWTYxMUFqamqN6YpEIoSEhCAqKgp5eXkoLy9HcXExcnJyAABJSUlQVlaGq6ur3P2TkpLg4uICVVXVBp1Pdb1795bZVtd1S0pKqrFmCwACAgLg7++Pr776CkpKSti3bx82bqTfxjcZFYBaGsaAGTOAmBhJ1X1UFPBv1XJDPX4MtG0r+WxuLlkAQImnhJVDVmL+yfn4adxP4CtTA0LyCvF49XoN1RJMnToVc+bMwddff42wsDB07tyZe5ivW7cOmzdvxqZNm2BnZwdNTU3MnTsXpaWlTXb8c+fOYfLkyVi2bBmEQiF0dXVx4MABbNiwocmOUVX1ggiPx4NYLK4x/oIFCxATE4P169fDysoK6urq8PLy4q6Burp6rcerK1xJSQmsWrsteW2SNKt9n+pz3eo6tpubGwQCASIiIsDn81FWVgYvL69a9yGvN2oE3dKsXi3p5q6kJBnluWfPRiUTEyMZKLqm19fDrYYjdUYq2uu0b3xeCXnDjB8/nvvvf8+ePfD39+faAyUmJsLd3R3vv/8+HBwc0KlTJ9y4caPeaXft2hV37txBXl4et+38+fNScc6ePQtzc3MsWrQIvXv3hrW1NW7fvi0Vh8/no6Kios5jJScno6ioiNuWmJgIJSUlvPXWW/XOc3WJiYnw8/ODp6cn7OzsYGxsjOzsbC7czs4OYrEYZ86ckbu/vb09EhISamxobWBgIHV9KioqkJaWVme+6nPd7O3tERsbW2MaKioq8PX1RVhYGMLCwjBhwoQ6C03k9UYFoJZk3z5g0SLJ561bgdGjG5XMxYuApyfw7Bnw00//bb/0zyX8/eRvbl1ZqXGNqgl5U2lpacHb2xtBQUHIy8uT6n1kbW2NmJgYnD17Funp6fjwww9x7969eqc9bNgwdOnSBb6+vkhOTkZCQgIWVf69VzlGTk4ODhw4gMzMTGzZsgURERFScSwsLJCVlYWkpCQ8fPgQJSUlMseaPHky1NTU4Ovri7S0NMTFxWHOnDmYMmUK9/qrMaytrXHkyBEkJSUhOTkZkyZNkqoxsrCwgK+vL/z9/REZGYmsrCzEx8fj0KFDAIDZs2fj6dOnmDBhAi5duoSbN2/ihx9+QEZGBgBgyJAhiIqKQlRUFK5fv44ZM2agoKCgXvmq67oFBwdj//79CA4ORnp6OlJTU7F27VqpONOmTcPp06cRHR0Nf3//Rl8n8nqgAlBLUlws6en18cfAzJmNSiIjQzJPalGRZHqLsDDJ9nuie3A/4I5eO3rhYu7FJsw0IW+WqVOn4smTJxAKhVLtdRYvXoyePXtCKBRi0KBBMDY2hoeHR73TVVJSQkREBIqLi9G3b19MmzYNK1eulIrz3nvvYd68eZg9ezYcHR1x9uxZmW7YY8eOxfDhwzF48GAYGBjI7YqvoaGBkydP4vHjx+jTpw+8vLwwdOhQbNu2rWEXo5qvvvoKbdq0gbOzM9zc3CAUCtGzWi11aGgovLy8MHPmTNjY2CAgIICriWrXrh1Onz4NkUgEV1dX9OrVCzt37uRexfn7+8PX1xc+Pj5wdXVFp06dMLgeg77W57oNGjQIhw8fxtGjR+Ho6IghQ4bgzz//lIpjbW0NZ2dn2NjYoF+/fi9zqchrgMeqv3AlePr0KXR1dVFYWNjgxocv7dIlyWsvpYaXTXNzAWdnyTxfvXsDp09LxvwpF5dj2J5hOHP7DLrqd8WFaRegLdB+BZknrd2LFy+QlZUFS0tLqKnRgJrk9cIYg7W1NWbOnIn58+crOjukBrX9zjTk+U2NoBXtwQNJI1F9fcm6nN4N9fH4MSAUSgo/XbpI5knV/reM89mpz3Dm9hlo8bVwxPsIFX4IIaSaBw8e4MCBA8jPz8cHH3yg6OyQZkAFIEV6/hxwcwMePZKUWKytG53UN98AV68CpqbAyZOSqS4A4PDVw9hwTtITItw9HDb6Nk2Rc0IIeaMYGhpCX18fO3bsQJs2NCJ+a9Ai2gB9/fXXsLCwgJqaGvr16yfzXra6w4cPw8bGBmpqarCzs+NG86wqPT0d7733HnR1daGpqYk+ffpwY1W0CJVzU1y4IKm+qaXraX18/jnw2WeSKS4sLCTbrj24hg9+kfwn86nzpxjbbexLZpoQQt5MjDE8ePAAkyZNUnRWSDNReAHo4MGDmD9/PoKDg/HXX3/BwcEBQqEQ9+/flxv/7NmzmDhxIqZOnYorV67Aw8MDHh4eUl0lMzMz8fbbb8PGxgbx8fFISUnBkiVLWlabhE8/BSIi/pubohFdUxn7r9ykpCTpQW9n91/4qoRVKCorwmCLwVg5dKX8RAghhJBWSOGNoPv164c+ffpwvRPEYjHMzMwwZ84cfPbZZzLxvb29UVRUhF9//ZXb1r9/fzg6OuLbb78FAEyYMAGqqqr44YcfGpWnV94Iets2YM4cyed9+4CJExuVzKefSqYL271bUo6qrqS8BMHxwZjvNB+GmoYvkWFC6ocaQRNCXrWmagSt0Bqg0tJSXL58GcOGDeO2KSkpYdiwYTh37pzcfc6dOycVHwCEQiEXXywWIyoqCl26dIFQKIShoSH69esnNUtydSUlJXj69KnU8socPSqZ3BQAVq1qdOFn3TrJcuCApLeXPAIVAdYMW0OFH0IIIaQahRaAHj58iIqKCpmBuYyMjJCfny93n/z8/Frj379/HyKRCGvWrMHw4cPx22+/wdPTE2PGjKlxdNLVq1dDV1eXW8zMzJrg7OSoqACWLJG8twoIkDTaaYTwcEntDyApBA0f/l/Y6azTWBS7CBXi2keKJYQQQlqzN64XWOWopO7u7pg3bx4AcANjffvtt3In6QsKCpIa8+Hp06evphCkrAycOgWsXw988YWk+3sDHTsGTJsm+bxggWSpdKfwDrx/8sbD5w/RVr0tPnb+uIkyTgghhLxZFFoDpK+vD2VlZZnh5O/duwdjY2O5+xgbG9caX19fHyoqKujWrZtUnK5du9bYC0wgEEBHR0dqeWUMDIC1a4FGzIb8xx/A+PGSiiRfX0kylUrKS+B12AsPnz9ED+MemNmncSNJE0Iab9CgQZg7dy63bmFhgU2bNtW6D4/Hq/UVfX01VTqkYZYsWYLp06e/krSvXbuGDh06SM3pRpqOQgtAfD4fvXr1kpqgTiwWIzY2Fk5OTnL3cXJykpnQLiYmhovP5/PRp08fbm6ZSjdu3IB55XTor6HnzwEvL+DFC8kUYTt3Sg8WHRgdiD9z/0QbtTb4efzPUFelSfwIqS83NzcMr/ouuYqEhATweDykpKQ0ON2LFy82+cMxJCQEjo6OMtvz8vIwYsSIJj0WqV1+fj42b94sNaebn58feDweeDweVFVVYWRkhHfeeQe7du2SmjcNkBSQK+NWXdasWQMA6NatG/r374+vvvqqWc+rtVB4N/j58+dj586d2L17N9LT0zFjxgwUFRVxI3H6+PggKCiIix8YGIjo6Ghs2LAB169fR0hICC5duoTZs2dzcT755BMcPHgQO3fuxK1bt7Bt2zYcO3YMMxs5v1ZLoKEB7N8PjBgBHDwoXYEUdiUM2y9vBw887Bu7D5ZtLBWXUUJeQ1OnTkVMTAzu3r0rExYWFobevXvD3t6+wekaGBhAQ0OjKbJYJ2NjYwgEgmY5VktSWlqqsGN/9913cHZ2lvnnevjw4cjLy0N2djZOnDiBwYMHIzAwEKNHj0Z5eblU3OXLlyMvL09qmVPZSxjABx98gNDQUJn9SBNgLcDWrVtZx44dGZ/PZ3379mXnz5/nwlxdXZmvr69U/EOHDrEuXbowPp/PbG1tWVRUlEya33//PbOysmJqamrMwcGBRUZG1js/hYWFDAArLCxs9Dk1l8v/XGaCFQKGELDl8csVnR3SyhUXF7Nr166x4uJiRWelQcrKypiRkRFbsWKF1PZnz54xLS0tFhoayh4+fMgmTJjATE1Nmbq6OuvevTvbt2+fVHxXV1cWGBjIrZubm7ONGzdy6zdu3GAuLi5MIBCwrl27st9++40BYBEREVycTz/9lFlbWzN1dXVmaWnJFi9ezEpLSxljjIWFhTEAUktYWBhjjMmkk5KSwgYPHszU1NRY27ZtWUBAAHv27BkX7uvry9zd3dm6deuYsbExa9u2LZs5cyZ3LHlu3brF3nvvPWZoaMg0NTVZ7969WUxMjFScFy9esE8//ZR16NCB8fl81rlzZ/bdd99x4WlpaWzUqFFMW1ubaWlpsbfffpvdunVL7vVjjDF3d3epZ4C5uTlbvnw5mzJlCtPW1ubCartulY4ePcp69+7NBAIBa9euHfPw8GCMMbZs2TJma2src74ODg5s8eLFNV4PW1tbtm3bNqltlde1utjYWAaA7dy5U+pcqn4/5CkpKWECgYCdOnWq1nitSW2/Mw15freIRtCzZ8+WqsGpKj4+XmbbuHHjMG7cuFrT9Pf3h7+/f1NkT2GePQOmTAFWrgRsbeXHyS7IhhJPCaO7jMaigYvkRyJEgRhjeF72XCHH1lDVAK8enQ1UVFTg4+OD8PBwLFq0iNvn8OHDqKiowMSJEyESidCrVy8sXLgQOjo6iIqKwpQpU9C5c2f07du3zmOIxWKMGTMGRkZGuHDhAgoLC6XaC1XS1tZGeHg4TE1NkZqaioCAAGhra+PTTz+Ft7c30tLSEB0djVOnTgEAdHV1ZdIoKiqCUCiEk5MTLl68iPv372PatGmYPXs2wsPDuXhxcXEwMTFBXFwcbt26BW9vbzg6OiIgIEDuOYhEIowcORIrV66EQCDAnj174ObmhoyMDHTs2BGApNb+3Llz2LJlCxwcHJCVlYWHDx8CAHJzczFw4EAMGjQIp0+fho6ODhITExtcu7F+/XosXboUwcHB9bpuABAVFQVPT08sWrQIe/bsQWlpKTeLgL+/P5YtW4aLFy+iT58+AIArV64gJSUFR44ckZuHx48f49q1a+hdz/kbhwwZAgcHBxw5cgTTKnuy1AOfz4ejoyMSEhIwdOjQeu9H6tYiCkBEVkkJ4OkJxMYC6enAtWuSTmTVjek6BhemXYCZrhmUeAp/o0mIjOdlz6G1WkshxxYFiaDJ16xXXH9/f6xbtw5nzpzBoEGDAEhef40dO5YbImNBlW6Xc+bMwcmTJ3Ho0KF6FYBOnTqF69ev4+TJkzA1NQUArFq1SqbdzuLFi7nPFhYWWLBgAQ4cOIBPP/0U6urq0NLSgoqKSo0dRQBg3759ePHiBfbs2QNNTcn5b9u2DW5ubli7di03lEibNm2wbds2KCsrw8bGBqNGjUJsbGyNBSAHBwc4ODhw6ytWrEBERASOHj2K2bNn48aNGzh06BBiYmK48do6derExf/666+hq6uLAwcOQPXf9/hdunSp89pVN2TIEHz8sXQv19quGwCsXLkSEyZMwLJly6TOBwA6dOgAoVCIsLAwrgAUFhYGV1dXqfxXlZOTA8YYdy/rw8bGRqYt2cKFC6XyDgAnTpyAi4sLt25qaorbt2/X+zikfqgA1AJVVEhqfmJjAS0tYO9e2cJPcVkx19DZzshOTiqEkIawsbGBs7Mzdu3ahUGDBuHWrVtISEjA8uXLAQAVFRVYtWoVDh06hNzcXJSWlqKkpKTebXzS09NhZmYm9cCU19nj4MGD2LJlCzIzMyESiVBeXt7gnqnp6elwcHDgCj8AMGDAAIjFYmRkZHAFIFtbWyhX+XExMTFBampqjemKRCKEhIQgKioKeXl5KC8vR3FxMdfDNikpCcrKynKHG6kMd3Fx4Qo/jSWv1qWu65aUlFRjwQ4AAgIC4O/vj6+++gpKSkrYt28fNm7cWGP84uJiAGjQiOeMMZkayU8++QR+fn5S29q3by+1rq6ujufPFVOL+iajAlALwxjwv/8Bhw9LGjpHRADV/9Z/vfErZkbNxKFxh9C/Q3/FZJSQetJQ1YAoSKSwYzfE1KlTMWfOHHz99dcICwtD586duYf5unXrsHnzZmzatAl2dnbQ1NTE3Llzm7QR7rlz5zB58mQsW7YMQqGQqy3ZsGFDkx2jquoFER6PJ9NTqaoFCxYgJiYG69evh5WVFdTV1eHl5cVdA3X12nuf1hWupKQEVm12prKyMpl4VQt2QP2uW13HdnNzg0AgQEREBPh8PsrKyuDl5VVjfH19fQDAkydPYGBgUGvaldLT02FpKd1JRV9fH1ZWVrXu9/jxY3Tu3LlexyD1RwWgFmbFCuCbbyRjJP74I1Bt1g/cenwL7x95H4UlhTiQdoAKQKTF4/F49X4NpWjjx49HYGAg9u3bhz179mDGjBncf+yJiYlwd3fH+++/D0DSpufGjRsyY47VpGvXrrhz5w7y8vJgYmICADh//rxUnLNnz8Lc3FyqW3X1Vx98Ph8VFbWP9N61a1eEh4ejqKiIKywkJiZCSUkJbzVi4uVKiYmJ8PPzg6enJwBJjVB2djYXbmdnB7FYjDNnzshMWQQA9vb22L17N8rKyuTWAhkYGCAvL49br6ioQFpaGgYPHlxrvupz3ezt7REbG8v1MK5ORUUFvr6+CAsLA5/Px4QJE2otNHXu3Bk6Ojq4du1avV7jnT59GqmpqdwAvQ2RlpZWa2GMNA41GmlBDhwAKtv0bd0qGfSwqudlzzHm4BgUlhTC2cwZX77zZfNnkpA3mJaWFry9vREUFIS8vDypVxPW1taIiYnB2bNnkZ6ejg8//FBmUNbaDBs2DF26dIGvry+Sk5ORkJAg9cCuPEZOTg4OHDiAzMxMbNmyBREREVJxLCwskJWVhaSkJDx8+BAlJSUyx5o8eTLU1NTg6+uLtLQ0xMXFYc6cOZgyZYrMVEINYW1tjSNHjiApKQnJycmYNGmSVI2RhYUFfH194e/vj8jISGRlZSE+Ph6HDh0CIOnw8vTpU0yYMAGXLl3CzZs38cMPP3Djtg0ZMgRRUVGIiorC9evXMWPGDBQUFNQrX3Vdt+DgYOzfvx/BwcFIT09Hamoq1lYdTRbAtGnTcPr0aURHR9fZiaZy3so//vhDJqykpAT5+fnIzc3FX3/9hVWrVsHd3R2jR4+Gj4+PVNxnz54hPz9faqk6H2V2djZyc3PlFijJS2rq7mlvAkV1gy8oYMzVlbGlS2XDxGIxm/zzZIYQMKN1Riz3aW6z5o2Q+nhdu8FXdfbsWQaAjRw5Umr7o0ePmLu7O9PS0mKGhoZs8eLFzMfHR6rLc13d4DMyMtjbb7/N+Hw+69KlC4uOjpbpvv7JJ5+wdu3aMS0tLebt7c02btzIdHV1ufAXL16wsWPHMj09vSbpBl9VYGAgc3V1rfHaZGVlscGDBzN1dXVmZmbGtm3bJnPOxcXFbN68eczExITx+XxmZWXFdu3axYUnJyezd999l2loaDBtbW3m4uLCMjMzGWOMlZaWshkzZrC2bdsyQ0NDtnr1arnd4OV1Ha/rujHG2M8//8wcHR0Zn89n+vr6bMyYMTLpuLi4yO0SL8/x48dZ+/btWUVFBbfN19eXG6JARUWFGRgYsGHDhrFdu3ZJxas8F1Qb1gAA+/DDD7k4q1atYkKhsF75aS2aqhs8j7FqL1wJnj59Cl1dXRQWFr7aaTHkKC2VtP2p3nN325/bMOfEHCjzlBHrEwtXC/mNDAlRpBcvXiArKwuWlpYNahxKSEvAGIO1tTVmzpwpNT9kbfH79euHefPmYeLEiU2en9LSUlhbW2Pfvn0YMGBAk6f/uqrtd6Yhz296BaZgly8Dmzf/t87nyxZ+Lty9gHknJe+N172zjgo/hBDSxB48eIBt27YhPz+/xnZC1fF4POzYseOVjdKck5ODzz//nAo/rwg1glagmzclU1s8eABoav43y3t1XQ26wq2LG1SVVTG3/9xmzSMhhLQGhoaG0NfXx44dO9CmTZt67+fo6Ch3bramYGVlVWcPMdJ4VABSkH/+Ad59V1L46dlTtsFzVToCHfw8/meUVJTUa1RbQgghDUOtQVofegWmAAUFwPDhQHY2YGUFHD8OyHtV+fvt37k/Sh6PBzUValNBCCGENAUqADWz4mLAzQ1ITQWMjYHffgPk9Uo9mHYQruGumHxkMsSs5oHJCCGEENJwVABqRmIxMGEC8McfgK4ucPIkUG1QUADA1ftXMfXoVABAR92ONMcXIYQQ0sToydqMlJQkIzurqwNHjwL29rJxCl8UYsyhMSgqK8JQy6H4YsgXzZ9RQggh5A1HBaBmNmcOkJkJDBwoGyZmYvj94ocbj27ATMcM+8fuh4oStVMnhBBCmhoVgBTg32mAZHyZ+CUir0eCr8zHT+N/goFm/SbYI4QQQkjDUAGohch9movgeMlEYFtHbEXf9n0VnCNCSEMNGjQIc+fO5dYtLCywadOmWvfh8XiIjIx86WM3VTqkYZYsWYLp06crOhstQnx8PHg8Xr3mb6vJtWvX0KFDBxQVFTVdxmpABaAWor1Oe/z2/m/4xPkTBPQMUHR2CGlV3NzcMHz4cLlhCQkJ4PF4SElJaXC6Fy9ebPKHY0hIiNyB9/Ly8jBixIgmPRapXX5+PjZv3iw1qa2fnx94PB54PB5UVVVhZGSEd955B7t27ZKaOBaQFJAr41Zd1qxZ09ynUqOavm/yODs7Iy8vD7q6uvWKX/0fBgDo1q0b+vfvj6+++qqBOW04KgC1IK4WrvjynS9psENCmtnUqVMRExODu3fvyoSFhYWhd+/esJfXa6EOBgYG0NDQaIos1snY2BgCgaBZjtWSlJaWKuzY3333HZydnWFubi61ffjw4cjLy0N2djZOnDiBwYMHIzAwEKNHj5aZNmP58uXIy8uTWubMmSP3ePHx8bCwsKh3/prz2pSVlYHP58PY2Piln2EffPABQkNDX9kUI5WoAKRgX/z+BdIfpCs6G4S0aqNHj4aBgQHCw8OltotEIhw+fBhTp07Fo0ePMHHiRLRv3x4aGhqws7PD/v37a023+iuwmzdvYuDAgVBTU0O3bt0QExMjs8/ChQvRpUsXaGhooFOnTliyZAnKysoAAOHh4Vi2bBmSk5O52oLKPFd/BZaamoohQ4ZAXV0d7dq1w/Tp0yESibhwPz8/eHh4YP369TAxMUG7du0wa9Ys7ljyZGZmwt3dHUZGRtDS0kKfPn1w6tQpqTglJSVYuHAhzMzMIBAIYGVlhe+//54Lv3r1KkaPHg0dHR1oa2vDxcUFmZmZAOTXCHh4eMDPz0/qmq5YsQI+Pj7Q0dHhathqu26Vjh07hj59+kBNTQ36+vrw9PQEICmEdO/eXeZ8HR0dsWTJkhqvx4EDB+Dm5iazXSAQwNjYGO3bt0fPnj3x+eef45dffsGJEydkvmPa2towNjaWWjQ1NWs8Zm0q7+nKlSthamqKt956CwBw584djB8/Hnp6emjbti3c3d2RnZ3N7RcfH4++fftCU1MTenp6GDBgAG7fvl3n9y00NBTvvfceNDU1sXLlSrmvwBITEzFo0CBoaGigTZs2EAqFePLkCfz8/HDmzBls3ryZS7syT++88w4eP36MM2fONOo61BcVgBTo+7++x5K4JXD63gkPnz9UdHYIeSUYA4qKFLPUd3YDFRUV+Pj4IDw8XGpKhMOHD6OiogITJ07Eixcv0KtXL0RFRSEtLQ3Tp0/HlClT8Oeff9brGGKxGGPGjAGfz8eFCxfw7bffYuHChTLxtLW1ER4ejmvXrmHz5s3YuXMnNm7cCADw9vbGxx9/DFtbW662wNvbWyaNoqIiCIVCtGnTBhcvXsThw4dx6tQpzJ49WypeXFwcMjMzERcXh927dyM8PFzmAV2VSCTCyJEjERsbiytXrmD48OFwc3NDTk4OF8fHxwf79+/Hli1bkJ6eju3bt0NLSwsAkJubi4EDB0IgEOD06dO4fPky/P39G/yf/vr16+Hg4IArV65wBZTarhsAREVFwdPTEyNHjsSVK1cQGxuLvn0lbS39/f2Rnp6OixcvcvGvXLmClJSUGidGffz4Ma5du4bevXvXK89DhgyBg4MDjhw50qBzbajY2FhkZGQgJiYGv/76K8rKyiAUCqGtrY2EhAQkJiZCS0sLw4cPR2lpKcrLy+Hh4QFXV1ekpKTg3LlzmD59Ong8Xp3ft5CQEHh6eiI1NRX+/v4yeUlKSsLQoUPRrVs3nDt3Dn/88Qfc3NxQUVGBzZs3w8nJCQEBAVzaZmZmAAA+nw9HR0ckJCS80msFRmQUFhYyAKywsPCVHeNS7iUmWCFgCAH74swXr+w4hDSn4uJidu3aNVZcXMxtE4kYkxRFmn8Rieqf9/T0dAaAxcXFcdtcXFzY+++/X+M+o0aNYh9//DG37urqygIDA7l1c3NztnHjRsYYYydPnmQqKiosNzeXCz9x4gQDwCIiImo8xrp161ivXr249eDgYObg4CATr2o6O3bsYG3atGGiKhcgKiqKKSkpsfz8fMYYY76+vszc3JyVl5dzccaNG8e8vb1rzIs8tra2bOvWrYwxxjIyMhgAFhMTIzduUFAQs7S0ZKWlpXLDq18/xhhzd3dnvr6+3Lq5uTnz8PCoM1/Vr5uTkxObPHlyjfFHjBjBZsyYwa3PmTOHDRo0qMb4V65cYQBYTk6O1HZfX1/m7u4udx9vb2/WtWtXbt3c3Jzx+Xymqakptfz+++9y94+Li2Pm5uY15snX15cZGRmxkpISbtsPP/zA3nrrLSYWi7ltJSUlTF1dnZ08eZI9evSIAWDx8fFy06zt+zZ37lyZ/AFgT548YYwxNnHiRDZgwIAa8yvvflfy9PRkfn5+csPk/c5Uasjzm2qAFODR80cYe2gsSipK4NbFDUEuQYrOEiGtno2NDZydnbFr1y4AwK1bt5CQkICpUyWjsldUVGDFihWws7ND27ZtoaWlhZMnT0rVftQmPT0dZmZmMDU15bY5OTnJxDt48CAGDBgAY2NjaGlpYfHixfU+RtVjOTg4SL1KGTBgAMRiMTIyMrhttra2UFZW5tZNTExw//79GtMViURYsGABunbtCj09PWhpaSE9PZ3LX1JSEpSVleHq6ip3/6SkJLi4uEBVVbVB51OdvFqXuq5bZW1ETQICArB//368ePECpaWl2Ldvn9xajUrFxcUAADW1+s/RyBiTaR/zySefICkpSWqpen5aWlrcMmLECOTk5Eht++ijj6TSs7OzA5/P59aTk5Nx69YtaGtrc/u0bdsWL168QGZmJtq2bQs/Pz8IhUK4ublh8+bNyMvLq9f51FX7Vdc1r426ujqeP3/eqH3ri0bZa2YV4gpMOjIJtwtvw6qtFfZ47qGpLsgbTUMDqNL0pNmP3RBTp07FnDlz8PXXXyMsLAydO3fmHubr1q3D5s2bsWnTJtjZ2UFTUxNz585t0oam586dw+TJk7Fs2TIIhULo6uriwIED2LBhQ5Mdo6rqBREejyfTU6mqBQsWICYmBuvXr4eVlRXU1dXh5eXFXQN1dfVaj1dXuJKSksys7PLaJFVvI1Of61bXsd3c3CAQCBAREQE+n4+ysjJ4eXnVGF9fXx8A8OTJExgY1G/MtvT0dFhWm/9IX18fVlZWNe6TlJTEfb5w4QIWLlyI+Ph4bptOtZm0q18bkUiEXr16Ye/evTJpV+Y7LCwM//vf/xAdHY2DBw9i8eLFiImJQf/+/Ws9n7raKtV1zWvz+PFjdO7cudH71wcVgJpZSHwIfsv8Deoq6jgy/gj01PQUnSVCXikeD2hkm85mN378eAQGBmLfvn3Ys2cPZsyYwf3HnpiYCHd3d7z//vsAJG16bty4gW7dutUr7a5du+LOnTvIy8uDyb+joZ4/f14qztmzZ2Fubi7Vrfr27dtScfh8PioqKuo8Vnh4OIqKiriHVGJiIpSUlLiGsY2RmJgIPz8/rvGwSCSSakxrZ2cHsViMM2fOYNiwYTL729vbY/fu3SgrK5NbC2RgYCBV+1BRUYG0tDQMHjy41nzV57rZ29sjNja2xjY9Kioq8PX1RVhYGPh8PiZMmFDrA7xz587Q0dHBtWvX0KVLl1rzBwCnT59Gamoq5s2bV2fcqqoWju7evQsVFZVaC0zV9ezZEwcPHoShoaFMYamqHj16oEePHggKCoKTkxP27duH/v371+v7VpPKa75s2TK54bWlnZaWVmsBtClQ1UMzKheX4487fwAAvnvvO9gZ2Sk4R4SQqrS0tODt7Y2goCDk5eVJ9T6ytrZGTEwMzp49i/T0dHz44Ye4d+9evdMeNmwYunTpAl9fXyQnJyMhIUHqgV15jJycHBw4cACZmZnYsmULIiIipOJYWFggKysLSUlJePjwIUpKSmSONXnyZKipqcHX1xdpaWmIi4vDnDlzMGXKFBgZGTXsolTL35EjR5CUlITk5GRMmjRJqsbIwsICvr6+8Pf3R2RkJLKyshAfH49Dhw4BAGbPno2nT59iwoQJuHTpEm7evIkffviBey03ZMgQREVFISoqCtevX8eMGTPqNahefa5bcHAw9u/fj+DgYKSnpyM1NRVr166VijNt2jScPn0a0dHRtb7+AiS1VcOGDcMff/whE1ZSUoL8/Hzk5ubir7/+wqpVq+Du7o7Ro0fDx8dHKu6zZ8+Qn58vtTx9+rTOc66vyZMnQ19fH+7u7khISODuyf/+9z/cvXsXWVlZCAoKwrlz53D79m389ttvuHnzJrp27Qqgft+3mgQFBeHixYuYOXMmUlJScP36dYSGhuLhw4dc2hcuXEB2djYePnzIfZeys7ORm5srtxDdpOpsJdQKvcpG0KXlpSwiPaLJ0yWkJaitceLr4uzZswwAGzlypNT2R48eMXd3d6alpcUMDQ3Z4sWLmY+Pj1SD19oaQTMmaST89ttvMz6fz7p06cKio6NlGkF/8sknrF27dkxLS4t5e3uzjRs3Ml1dXS78xYsXbOzYsUxPT48BYGFhYYwxJpNOSkoKGzx4MFNTU2Nt27ZlAQEB7NmzZ1y4vMa6gYGBzNXVtcZrk5WVxQYPHszU1dWZmZkZ27Ztm8w5FxcXs3nz5jETExPG5/OZlZUV27VrFxeenJzM3n33XaahocG0tbWZi4sLy8zMZIwxVlpaymbMmMHatm3LDA0N2erVq+U2gq56Tet73Rhj7Oeff2aOjo6Mz+czfX19NmbMGJl0XFxcmK2tbY3XoKrjx4+z9u3bs4qKCm6br68vA8AAMBUVFWZgYMCGDRvGdu3aJRWv8lwq41ZdPvzwQ7nHq08jaHkNsPPy8piPjw/T19dnAoGAderUiQUEBLDCwkKWn5/PPDw8uPtlbm7Oli5dyuW1vt+3yvyhSiNoxhiLj49nzs7OTCAQMD09PSYUCrnwjIwM1r9/f6aurs4AsKysLMYYY6tWrWJCobDG82yqRtC8f0+EVPH06VPo6uqisLCw1ipDQoi0Fy9eICsrC5aWlg1qHEpIS8AYg7W1NWbOnIn58+fXK36/fv0wb948TJw4sRly+OYrLS2FtbU19u3bhwEDBsiNU9vvTEOe3/QKjBBCSKv34MEDbNu2Dfn5+TW2E6qOx+Nhx44dr3zE4tYkJycHn3/+eY2Fn6ZEjaAJIYS0eoaGhtDX18eOHTvQpk2beu/n6OhY77mySN2srKwa1Mj7ZVABiBBCSKtHrUFaH3oFRgghhJBWhwpAhBBCCGl1WkQB6Ouvv4aFhQXU1NTQr1+/OicXPHz4MGxsbKCmpgY7OzscP35cKtzPz4+bXbZyGT58+Ks8BUJIFfQ6gRDyqjTV74vCC0AHDx7E/PnzERwcjL/++gsODg4QCoU1zkdz9uxZTJw4EVOnTsWVK1fg4eEBDw8PpKWlScUbPnw4N8NsXl4e9u/f3xynQ0irVjm676uew4cQ0npV/r687JxyCh8HqF+/fujTpw+2bdsGQDK8vJmZGebMmYPPPvtMJr63tzeKiorw66+/ctv69+8PR0dHfPvttwAkNUAFBQWIjIxsVJ5oHCBCGi8vLw8FBQUwNDSEhoaGzOSPhBDSGIwxPH/+HPfv34eenh43pUxVDXl+K7QXWGlpKS5fvoygoP9mQ68cXvzcuXNy9zl37pzMAFVCoVCmsBMfHw9DQ0O0adMGQ4YMwRdffIF27do1+TkQQqQZGxsDQK2zihNCSGPp6elxvzMvQ6EFoIcPH6KiokJmbhojIyNcv35d7j75+fly4+fn53Prw4cPx5gxY2BpaYnMzEx8/vnnGDFiBM6dOwdlZWWZNEtKSqTmN2nKeVgIaW14PB5MTExgaGgodyZvQghpLFVVVbnP8cZ4I8cBmjBhAvfZzs4O9vb26Ny5M+Lj4zF06FCZ+KtXr65xtlpCSOMoKys32Q8VIYQ0NYU2gtbX14eysrLMjMr37t2rsXrL2Ni4QfEBoFOnTtDX18etW7fkhgcFBaGwsJBb7ty508AzIYQQQsjrRKEFID6fj169eiE2NpbbJhaLERsbCycnJ7n7ODk5ScUHgJiYmBrjA8Ddu3fx6NEjuQ2mAEAgEEBHR0dqIYQQQsibS+Hd4OfPn4+dO3di9+7dSE9Px4wZM1BUVMRNRufj4yPVSDowMBDR0dHYsGEDrl+/jpCQEFy6dAmzZ88GAIhEInzyySc4f/48srOzERsbC3d3d1hZWUEoFCrkHAkhhBDSsii8DZC3tzcePHiApUuXIj8/H46OjoiOjuYaOufk5EBJ6b9ymrOzM/bt24fFixfj888/h7W1NSIjI9G9e3cAknYHKSkp2L17NwoKCmBqaop3330XK1asgEAgqFeeKkcGoMbQhBBCyOuj8rldnxF+FD4OUEt09+5dmJmZKTobhBBCCGmEO3fuoEOHDrXGoQKQHGKxGP/88w8YY+jYsSPu3LlD7YJamKdPn8LMzIzuTQtD96XlonvTMtF9aVqMMTx79gympqZSb4/kUfgrsJZISUkJHTp04KrSqGF0y0X3pmWi+9Jy0b1pmei+NB1dXd16xVN4I2hCCCGEkOZGBSBCCCGEtDpUAKqFQCBAcHBwvXuPkeZD96ZlovvSctG9aZnovigONYImhBBCSKtDNUCEEEIIaXWoAEQIIYSQVocKQIQQQghpdagARAghhJBWp9UVgFavXo0+ffpAW1sbhoaG8PDwQEZGhlScFy9eYNasWWjXrh20tLQwduxY3Lt3TypOTk4ORo0aBQ0NDRgaGuKTTz5BeXl5c57KGyc0NBT29vbcgGBOTk44ceIEF073pWVYs2YNeDwe5s6dy22je6MYISEh4PF4UouNjQ0XTvdFcXJzc/H++++jXbt2UFdXh52dHS5dusSFM8awdOlSmJiYQF1dHcOGDcPNmzel0nj8+DEmT54MHR0d6OnpYerUqRCJRM19Km8u1soIhUIWFhbG0tLSWFJSEhs5ciTr2LEjE4lEXJyPPvqImZmZsdjYWHbp0iXWv39/5uzszIWXl5ez7t27s2HDhrErV66w48ePM319fRYUFKSIU3pjHD16lEVFRbEbN26wjIwM9vnnnzNVVVWWlpbGGKP70hL8+eefzMLCgtnb27PAwEBuO90bxQgODma2trYsLy+PWx48eMCF031RjMePHzNzc3Pm5+fHLly4wP7++2928uRJduvWLS7OmjVrmK6uLouMjGTJycnsvffeY5aWlqy4uJiLM3z4cObg4MDOnz/PEhISmJWVFZs4caIiTumN1OoKQNXdv3+fAWBnzpxhjDFWUFDAVFVV2eHDh7k46enpDAA7d+4cY4yx48ePMyUlJZafn8/FCQ0NZTo6OqykpKR5T+AN16ZNG/bdd9/RfWkBnj17xqytrVlMTAxzdXXlCkB0bxQnODiYOTg4yA2j+6I4CxcuZG+//XaN4WKxmBkbG7N169Zx2woKCphAIGD79+9njDF27do1BoBdvHiRi3PixAnG4/FYbm7uq8t8K9LqXoFVV1hYCABo27YtAODy5csoKyvDsGHDuDg2Njbo2LEjzp07BwA4d+4c7OzsYGRkxMURCoV4+vQprl692oy5f3NVVFTgwIEDKCoqgpOTE92XFmDWrFkYNWqU1D0A6G9G0W7evAlTU1N06tQJkydPRk5ODgC6L4p09OhR9O7dG+PGjYOhoSF69OiBnTt3cuFZWVnIz8+Xuje6urro16+f1L3R09ND7969uTjDhg2DkpISLly40Hwn8wZr1QUgsViMuXPnYsCAAejevTsAID8/H3w+H3p6elJxjYyMkJ+fz8Wp+oNRGV4ZRhovNTUVWlpaEAgE+OijjxAREYFu3brRfVGwAwcO4K+//sLq1atlwujeKE6/fv0QHh6O6OhohIaGIisrCy4uLnj27BndFwX6+++/ERoaCmtra5w8eRIzZszA//73P+zevRvAf9dW3rWvem8MDQ2lwlVUVNC2bVu6N02kVc8GP2vWLKSlpeGPP/5QdFbIv9566y0kJSWhsLAQP/30E3x9fXHmzBlFZ6tVu3PnDgIDAxETEwM1NTVFZ4dUMWLECO6zvb09+vXrB3Nzcxw6dAjq6uoKzFnrJhaL0bt3b6xatQoA0KNHD6SlpeHbb7+Fr6+vgnNHKrXaGqDZs2fj119/RVxcHDp06MBtNzY2RmlpKQoKCqTi37t3D8bGxlyc6j0pKtcr45DG4fP5sLKyQq9evbB69Wo4ODhg8+bNdF8U6PLly7h//z569uwJFRUVqKio4MyZM9iyZQtUVFRgZGRE96aF0NPTQ5cuXXDr1i36m1EgExMTdOvWTWpb165dudeTlddW3rWvem/u378vFV5eXo7Hjx/TvWkira4AxBjD7NmzERERgdOnT8PS0lIqvFevXlBVVUVsbCy3LSMjAzk5OXBycgIAODk5ITU1VerLGRMTAx0dHZkvPXk5YrEYJSUldF8UaOjQoUhNTUVSUhK39O7dG5MnT+Y+071pGUQiETIzM2FiYkJ/Mwo0YMAAmeFVbty4AXNzcwCApaUljI2Npe7N06dPceHCBal7U1BQgMuXL3NxTp8+DbFYjH79+jXDWbQCim6F3dxmzJjBdHV1WXx8vFTX0efPn3NxPvroI9axY0d2+vRpdunSJebk5MScnJy48Mquo++++y5LSkpi0dHRzMDAgLqOvqTPPvuMnTlzhmVlZbGUlBT22WefMR6Px3777TfGGN2XlqRqLzDG6N4oyscff8zi4+NZVlYWS0xMZMOGDWP6+vrs/v37jDG6L4ry559/MhUVFbZy5Up28+ZNtnfvXqahocF+/PFHLs6aNWuYnp4e++WXX1hKSgpzd3eX2w2+R48e7MKFC+yPP/5g1tbW1A2+CbW6AhAAuUtYWBgXp7i4mM2cOZO1adOGaWhoME9PT5aXlyeVTnZ2NhsxYgRTV1dn+vr67OOPP2ZlZWXNfDZvFn9/f2Zubs74fD4zMDBgQ4cO5Qo/jNF9aUmqF4Do3iiGt7c3MzExYXw+n7Vv3555e3tLjTVD90Vxjh07xrp3784EAgGzsbFhO3bskAoXi8VsyZIlzMjIiAkEAjZ06FCWkZEhFefRo0ds4sSJTEtLi+no6LAPPviAPXv2rDlP443GY4wxRdZAEUIIIYQ0t1bXBogQQgghhApAhBBCCGl1qABECCGEkFaHCkCEEEIIaXWoAEQIIYSQVocKQIQQQghpdagARAghhJBWhwpAhBBCCGl1qABECHltPXjwADNmzEDHjh0hEAhgbGwMoVCIxMREAACPx0NkZKRiM0kIaZFUFJ0BQghprLFjx6K0tBS7d+9Gp06dcO/ePcTGxuLRo0eKzhohpIWjqTAIIa+lgoICtGnTBvHx8XB1dZUJt7CwwO3bt7l1c3NzZGdnAwB++eUXLFu2DNeuXYOpqSl8fX2xaNEiqKhI/ifk8Xj45ptvcPToUcTHx8PExARffvklvLy8muXcCCGvHr0CI4S8lrS0tKClpYXIyEiUlJTIhF+8eBEAEBYWhry8PG49ISEBPj4+CAwMxLVr17B9+3aEh4dj5cqVUvsvWbIEY8eORXJyMiZPnowJEyYgPT391Z8YIaRZUA0QIeS19fPPPyMgIADFxcXo2bMnXF1dMWHCBNjb2wOQ1ORERETAw8OD22fYsGEYOnQogoKCuG0//vgjPv30U/zzzz/cfh999BFCQ0O5OP3790fPnj3xzTffNM/JEUJeKaoBIoS8tsaOHYt//vkHR48exfDhwxEfH4+ePXsiPDy8xn2Sk5OxfPlyrgZJS0sLAQEByMvLw/Pnz7l4Tk5OUvs5OTlRDRAhbxBqBE0Iea2pqanhnXfewTvvvIMlS5Zg2rRpCA4Ohp+fn9z4IpEIy5Ytw5gxY+SmRQhpHagGiBDyRunWrRuKiooAAKqqqqioqJAK79mzJzIyMmBlZSWzKCn995N4/vx5qf3Onz+Prl27vvoTIIQ0C6oBIoS8lh49eoRx48bB398f9vb20NbWxqVLl/Dll1/C3d0dgKQnWGxsLAYMGACBQIA2bdpg6dKlGD16NDp27AgvLy8oKSkhOTkZaWlp+OKLL7j0Dx8+jN69e+Ptt9/G3r178eeff+L7779X1OkSQpoYNYImhLyWSkpKEBISgt9++w2ZmZkoKyuDmZkZxo0bh88//xzq6uo4duwY5s+fj+zsbLRv357rBn/y5EksX74cV65cgaqqKmxsbDBt2jQEBAQAkDSC/vrrrxEZGYnff/8dJiYmWLt2LcaPH6/AMyaENCUqABFCSDXyeo8RQt4s1AaIEEIIIa0OFYAIIYQQ0upQI2hCCKmGWgYQ8uajGiBCCCGEtDpUACKEEEJIq0MFIEIIIYS0OlQAIoQQQkirQwUgQgghhLQ6VAAihBBCSKtDBSBCCCGEtDpUACKEEEJIq0MFIEIIIYS0Ov8H6Un4mDy8K30AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "steps = [(epoch+1)*train_steps for epoch in range(epochs)]\n",
    "plt.plot(steps, history_standard[\"factorized_top_k/top_100_categorical_accuracy\"], color=\"r\", linestyle=\"--\", label=\"Train accuracy\")\n",
    "plt.plot(steps, history_de[\"factorized_top_k/top_100_categorical_accuracy\"], color=\"g\", linestyle=\"--\", label=\"Train accuracy (DE)\")\n",
    "plt.plot(steps, history_de_restrict[\"factorized_top_k/top_100_categorical_accuracy\"], color=\"b\", linestyle=\"--\", label=\"Train accuracy (DE+restrict)\")\n",
    "plt.plot(steps, history_standard[\"val_factorized_top_k/top_100_categorical_accuracy\"], color=\"r\", label=\"Validation accuracy\")\n",
    "plt.plot(steps, history_de[\"val_factorized_top_k/top_100_categorical_accuracy\"], color=\"g\", label=\"Validation accuracy (DE)\")\n",
    "plt.plot(steps, history_de_restrict[\"val_factorized_top_k/top_100_categorical_accuracy\"], color=\"b\", label=\"Validation accuracy (DE+restrict)\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Step\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Model accuracy with and wo dynamic embeddings\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7ca8455",
   "metadata": {},
   "source": [
    "## Plotting embedding sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4bff27c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "steps = history_de_size[\"step\"]\n",
    "plt.plot(steps, history_de_size[\"embedding_size_user\"], color=\"r\", label=\"User embedding\")\n",
    "plt.plot(steps, history_de_size_restrict[\"embedding_size_user\"], color=\"r\", linestyle=\":\", label=\"User embedding (restrict)\")\n",
    "plt.axhline(de_model.lookup_vocab_sizes[\"user\"], color=\"r\", linestyle=\"--\")\n",
    "plt.plot(steps, history_de_size[\"embedding_size_movie\"], color=\"g\", label=\"Movie embedding\")\n",
    "plt.plot(steps, history_de_size_restrict[\"embedding_size_movie\"], color=\"g\", linestyle=\":\", label=\"Movie embedding (restrict)\")\n",
    "plt.axhline(de_model.lookup_vocab_sizes[\"movie\"], color=\"g\", linestyle=\"--\")\n",
    "plt.ylabel(\"Embedding size\")\n",
    "plt.xlabel(\"Step\")\n",
    "plt.title(\"Changes in the embedding size over time\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e88bd34a",
   "metadata": {},
   "source": [
    "## Some EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "64c8e204",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    100000.000000\n",
      "mean          3.902200\n",
      "std           1.728747\n",
      "min           2.000000\n",
      "50%           3.000000\n",
      "90%           6.000000\n",
      "max          15.000000\n",
      "Name: movie_title, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = tfds.as_dataframe(raw_ratings_dataset.shuffle(buffer_size=10000, seed=2023).take(100000))\n",
    "\n",
    "# Getting the 90th percentile of movie title lengths\n",
    "movie_title_len = df[\"movie_title\"].str.decode(\"utf-8\").str.lower().str.replace(punctuation_regex, \" \", regex=True).str.split().str.len().describe(percentiles=[0.9])\n",
    "print(movie_title_len)\n",
    "\n",
    "# Plotting frequencies of different IDs/tokens\n",
    "user_id_ser = df[\"user_id\"].str.decode(\"utf-8\").value_counts().iloc[:100]\n",
    "movie_title_ser = df[\"movie_title\"].str.decode(\"utf-8\").str.lower().str.replace(punctuation_regex, \" \", regex=True).str.split().explode().value_counts().iloc[:100]\n",
    "\n",
    "\n",
    "plt.subplots(2,1, figsize=(12, 8))\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "user_id_ser.plot.bar()\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "movie_title_ser.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "556fb6b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b159d303",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
